Miracle-dz/UniFAR-FTUs huggingface dataset:
Land cover classification by SAR

A 7.5 km/spl times/12.4 km test site located in northern Michigan was used to evaluate the land-cover classification accuracy attainable by automatic classifiers using various combinations of SAR polarizations and frequency bands. The scene was imaged by the JPL AirSAR at L- and C-bands. Using all available polarimetric data at both bands, a level-1 classifier was developed capable of correctly classifying each pixel in the scene as belonging to one of the following four categories: urban, tall vegetation (trees), short vegetation (grasses and crops), and bare surfaces (water, roads and bare soil) with an accuracy of 95% or better. The classification results provided by this L/C polarimetric combination are compared in this study with results based on L-band hh-polarization alone (representing the JERS-1 SAR), C-band vv-polarization alone (representing ERS-1 SAR), both of the preceding channels in combination, as well as other frequency/polarization combinations. The strengths and weaknesses of the different combinations are evaluated and related to the physical interactions taking place. Subsequently, a level-2 classifier was then developed to distinguish between three different tree types. Again, a comparison was made with the various combinations, and an analysis is given of the important physical interactions. Lastly, a study was made of the importance of resolution and number of looks in regard to the use of texture for classification. <<ETX>>
The RAVE/VERTIGO vertex reconstruction toolkit and framework

A detector-independent toolkit for vertex reconstruction (RAVE1) is being developed, along with a standalone framework (VERTIGO2) for testing, analyzing and debugging. The core algorithms represent state-of-the-art for geometric vertex finding and fitting by both linear (Kalman filter) and robust estimation methods. Main design goals are ease of use, flexibility for embedding into existing software frameworks, extensibility, and openness. The implementation is based on modern object-oriented techniques, is coded in C++ with interfaces for Java and Python, and follows an open-source approach. A beta release is available.
Autostereoscopic desktop display: an evolution of technology

A new technology for creating a large stereoscopic image has been developed and has evolved over several years. The optical apparatus for creating a large, distortion-free image has changed from a bulky, immersive viewing system to a display that can sit on a desktop and creates a comfortable stereo image that can be viewed for long periods of time without eyestrain. The central idea of creating the images with a monocentric optical system has remained constant; however, the application of monocentricity has changed over several designs. A monocentric design is one where multiple spherical optical surfaces share the same center of curvature. The advantage of this type of system is that it allows the image quality to be corrected over a very wide field of view with a large pupil. The first system was presented at the Stereoscopic Displays and Applications Conference in 2003. This system was based upon custom digital projectors creating images on two curved diffusers, which were then imaged by a ball lens. The final collimation of the images was done with a 36-inch radius mirror. This system was designed as proof of a concept for the technology, and it was not practical to market it as a product solution. This led to a desktop solution that utilized twin LCD displays with monocentric imaging engines that had separate collimating mirrors. There were various improvements to this configuration that ultimately resulted in a high-resolution, bright, low-distortion stereo image. After a brief review of the previous technology, the various embodiments of the desktop display will be discussed.
Process analysis and abnormal situation detection: from theory to practice

The article discusses the use of latent variable models based on historical data and examines their potential and limitations for improving operations for both batch and continuous processes. The use of these models for multivariate statistical process monitoring, abnormal situation detection, and fault diagnosis is demonstrated. Examples from state-of-the-art major industrial applications currently running online illustrate the tremendous potential of these methods. In this context, an industrial application for abnormal situation detection is defined as "state of the art" if it has been operational several years after it was commissioned, has generated large savings, has been operating safely and/or has improved safety conditions in the plant, and is accepted enthusiastically by the operators. Such an application could be based entirely on known theory, but frequently it includes company proprietary modifications to suit the particular operating characteristics of the process. The article contains an extensive literature review on the subject and practical considerations for the user, as well as warnings about potential pitfalls in areas ranging from data acquisition to modeling to online application.
An Alphanet approach to optimising input transformations for continuous speech recognition

The authors extend to continuous speech recognition (CSR) the Alphanet approach to integrating backprop networks and HMM (hidden Markov model)-based isolated word recognition. They present the theory of a method for discriminative training of components of a CSR system, using training data in the form of complete sentences. The derivatives of the discriminative score with respect to the parameters are expressed in terms of the posterior probabilities of state occupancies (gammas) under two conditions called 'clamped' and 'free' because they correspond to the two conditions in Boltzmann machine training. The authors compute these clamped and free gammas using the forward-backward algorithm twice, and use the differences to drive the adaptation of a preprocessing data transformation, which can be thought of as replacing the linear transformation which yields MFCCs, or which normalizes a grand covariance matrix. <<ETX>>
Integrated position and motion tracking method for online multi-vehicle tracking-by-detection

In this paper, we present a multi-vehicle tracking method that uses integrated position and motion tracking methods to minimize missing and false detection. No existing state-of-the-art vehicle detection method can detect all the vehicles on the road and remove all false positive alarms. Therefore, a robust tracking-by-detection algorithm is necessary to minimize the number of false positive and false negative alarms. In multi-vehicle tracking, there are three types of errors such as false negative alarms, false positive alarms, and track identity switches. False negative and false positive alarms are caused by an imperfect detection algorithm, while track identity switches are caused by measurement-to-track pair confusion. Our tracking-by-detection method minimizes these errors while processing in real-time for online application. Sparse false positive alarms are reduced by a track initialization procedure. Motion tracking with selected features can minimize false negative alarms. A data association algorithm with complementary global and local distance prevents tracks from connecting measurements incorrectly. The proposed method was evaluated and verified in challenging, real road environments. The experimental results demonstrate that our multi-vehicle tracking method remarkably reduces false positive and false negative alarms and performs better than previous methods.
A Process Variation Tolerant Embedded Split-Gate Flash Memory Using Pre-Stable Current Sensing Scheme

Replica-cell sensing schemes are commonly used in the read circuits of flash memories to provide the appropriate reference current across various process, voltage and temperature (PVT) conditions. However, process variation on the replica array causes fluctuations in the settling time and the value of the reference current across dies or wafers, especially in split-gate flash memories. A long settling time of reference current slows down the access time, and causes ringing on outputs. Fluctuation in the reference current produces various sensing margins, and decreases the yield, due to tail bits. A circuit-level technique for embedded flash memories, called pre-stable current sensing (PSCS), is proposed to reduce the fluctuation in access time and sensing margin, without additional masks or process steps. Experiments on fabricated flash macros (4 Mb, 2 Mb, 1 Mb, and 512 Kb) using a 0.25 mum embedded flash process demonstrate that PSCS achieves uniform access time across hundreds of samples. Additionally, PSCS works with a wide range of supply voltages (1.1-3 V).
Design and Implementation of Adaptive Dynamic Load Balancing Distributed Parallel Rendering System Based on Sort-First

The sort-first algorithm is the most simple distributed rendering solutions, but there is a problem of loading balancing between nodes, so sort-first adaptive dynamic load balancing algorithm proposed in this paper. This algorithm uses feedback data what comes from rending node to solve the problem of unbalanced tasks between nodes in sort-first algorithm, while the use of its distributed rendering system can achieve a simple expansion. This algorithm can provide a good reference for similar system design.
Associative dynamics of color images in a large-scale chaotic neural network

In this paper, we report a way to store color images in a large-scale chaotic neural network and to retrieve them by using chaotic dynamics. In the proposed method, color images are converted to binary codes, modified slightly by inverting a few bits, and stored in the network. The results of numerical simulations show that chaotic transitions among stored patterns and their reverse patterns can be observed within a certain range of parameters. We also compare five different coding schemes of color information, which change the appearance of chaotic dynamics. In addition, if connections are restricted in a neighborhood of each unit, a variety of wave patterns are observed.
Using blogs in academic libraries: versatile information platforms

Purpose – The purpose of the paper was to provide an in‐depth discussion of the poster session, Keep Blogging Along: Side by Side Library Blogs, presented at the American Library Association's Annual Conference in Washington, DC, June 27, 2007.Design/methodology/approach – Complete with each blog discussed is a distinction of purpose, basic elements of blog design including organizational labels and tags, enhancement and usability tools, and a statement of success or failure for each project.Findings – Comprehensive information concerning three academic library blogs; topics of collection development, children's literature review, a collaborative professional development project, and a supplemental academic course blog is provided.Practical implications – To make available basic tools and resources for readers to create a blog, or blogs; keep statistical and evaluative information on individual projects; and enhance project usability with tools and widgets.Originality/value – This paper details various in.
Web Image Annotation with Semi-supervised Feature Selection

Web image annotation has become a critical research issue in recent years. It is beneficial to develop effective semisupervised feature selection methods to exploit the labeled data and unlabeled data simultaneously for web image annotation. However, the graph Laplacian based semi-supervised learning methods suffer from the fact that sparse coordinates are biased toward a constant and the Laplacian embedding often cannot preserve local topology well as we expected. In this paper we propose a novel semi-supervised feature selection method based on the second-order Hessian energy, namely Hessian Semisupervised Feature Selection (HSFS), which can overcome the drawbacks of Laplacian based methods. Extensive experiments are performed and the results show that our method is more suitable than Lapacian based methods for large-scale web image annotation.
Automatic Tailored Multi -Paper Summarization based on Rhetorical Document Profile and Summary Specification

In order to assist researchers in addressing time constraint and low relevance in using scientific articles, an automatic tailored multi-paper summarization (TMPS) is proposed. In this paper, we extend Teufel’s tailored summary to deal with multi-papers and more flexible representation of user information needs. Our TMPS extracts Rhetorical Document Profile (RDP) from each paper and presents a summary based on user information needs. Building Plan Language (BPLAN) is introduced as a formalization of Teufel’s building plan and used to represent summary specification, which is more flexible representation user information needs. Surface repair is embedded within the BPLAN for improving the readability of extractive summary. Our experiment shows that the average performance of RDP extraction module is 94.46%, which promises high quality of extracts for summary composition. Generality evaluation shows that our BPLAN is flexible enough in composing various forms of summary. Subjective evaluation provides evidence that surface repair operators can improve the resulting summary readability.
An approach to the segmentation of multi-page document flow using binary classification

In this paper, we present a method for segmentation of document page flow applied to heterogeneous real bank documents. The approach is based on the content of images and it also incorporates font based features inside the documents. Our method involves a bag of visual words (BoVW) model on the designed image based feature descriptors and a novel approach to combine the consecutive pages of a document into a single feature vector that represents the transition between these pages. The transitions here could be represented by one of the two different classes: continuity of the same document or beginning of a new document. Using the transition feature vectors, we utilize three different binary classifiers to make predictions on the relationship between consecutive pages. Our initial results demonstrate that the proposed method can exhibit promising performance for document flow segmentation at this stage.
High frequency passive microwave radiometry over a snow-covered surface in Alaska

Millimeter-wave Imaging Radiometer (MIR) data (ranging in frequency from 89 GHz to 325 GHz) collected from NASA ER-2 flights over Alaska in April 1995, are used to identify clouds, vegetation type, and snow cover. The procedure used is as follows: 1. ) Determine whether a purely MIR-based cloud detection scheme is possible over a snow-covered surface; 2. ) Analyze the influence of changing vegetation type on the brightness temperatures; and 3. ) Compare completely snow-covered scenes with partially snow-covered and snow-free regions for cloudy and clear sky periods to determine whether varying snow conditions affect the MIR data. Results show that the determination of cloudy pixels over a snow-covered surface is not possible using a simple brightness temperature threshold technique. Furthermore, it is concluded that while no statistical discrimination between specific vegetation classes can be made, statistical significance is obtained when the vegetation is grouped into two classes only, for example vegetated and barren. It is also shown that the state of the snow cover (complete coverage; melting; or patchy) has a distinct affect on these results.
Predicting operator mental workload using a time-based algorithm

Existing workload algorithms based on the Multiple Resources Theory (MRT) provide an effective approach for diagnosing operator overload caused by interference among concurrent tasks, however, their ability to handle overload in single task conditions is limited. We argue a time-based algorithm, developed on the Information Processing (IP) model of workload (Hendy, Liao, & Milgram, 1997), provides a viable solution to address this limitation. In this study, we proposed a new algorithmic implementation of the IP model in the context of task network modeling. The new algorithm was implemented in a JAVA program and tested on an existing model of a Bakan vigilance task. The results obtained from the new algorithm demonstrated the feasibility of this solution. By integrating resource-based and time-based algorithms, analysts will be able to diagnose more accurately system performance breakdowns caused by operator overload.
Control Charts for Monitoring Fault Signatures: Cuscore versus GLR

It is commonly assumed that an assignable cause may shift a process mean persistently to an unknown but constant value. However, there are situations such that a mean change is not persistently constant but time varying, which is known as the signature of a fault. Different forms of fault signatures may arise, especially when a residuals control chart is used to monitor an autocorrelated or closed‐loop controlled process. Incorporating a priori knowledge of a fault signature, fault‐sensitized control charts may be developed. In this paper, we investigate two control charts of this kind: generalized likelihood ratio (GLR) and cumulative score (Cuscore) charts. A sine wave representing a bounded signal and a linear trend representing an unbounded signal are used as fault signatures for investigation purposes. Two different cases are analyzed, a known fault signature and parameter and a known fault signature but unknown parameter. Both the initial performances and steady‐state performances of these charts are analyzed. The former case is somewhat artificial since it is synchronizing the control statistic and the fault signature; the latter one is more realistic since time of the occurrence of a fault is unknown. Simulation results show that GLR charts are robust to unknown signal start times and perform better than the alternatives for the cases compared. Copyright © 2003 John Wiley & Sons, Ltd.
A 14-bit 80-kHz sigma-delta A/D converter: modeling, design and performance evaluation

The development is described of a sigma-delta A/D (analog-to-digital) converter. Included is a brief overview of sigma-delta conversion. The A/D converter achieves an 88.5-dB dynamic range and a maximum signal-to-noise ratio of 81.5 dB. The harmonic distortion is negligible. This level of performance is about 10 dB higher than previously reported results for oversampled A/D converters in this frequency range. The analog modulator uses a double-integration switched-capacitor architecture with an oversampling rate of 10.24 MHz. Transconductance amplifiers having a 160-MHz f/sub t/ were developed for the integrators. The circuit is implemented in a 1.75- mu m 5-V CMOS process. The analog circuitry occupies 2 mm/sup 2/ of silicon area and consumes 75 mW of power. Some of the difficult problems associated with evaluating the performance of sigma-delta converters are described. The design of a sigma-delta development and performance evaluation system is presented. This system includes a custom interface board linking the chip to a Sun workstation, and extensive digital signal processing and analysis software. >
Sensitivity of Simulated Water Temperature to Vertical Mixing Scheme and Water Turbidity in the Yellow Sea

Accurate prediction of sea water temperature has been emphasized to make precise local weather forecast and to understand change of ecosystem. The Yellow Sea, which has turbid water and strong tidal current, is an unique shallow marginal sea. It is essential to include the effects of the turbidity and the strong tidal mixing for the realistic simulation of temperature distribution in the Yellow Sea. Evaluation of ocean circulation model response to vertical mixing scheme and turbidity is primary objective of this study. Three-dimensional ocean circulation model(Regional Ocean Modeling System) was used to perform numerical simulations. Mellor- Yamada level 2.5 closure (M-Y) and K-Profile Parameterization (KPP) scheme were selected for vertical mixing parameterization in this study. Effect of Jerlov water type 1, 3 and 5 was also evaluated. The simulated temperature distribution was compared with the observed data by National Fisheries Research and Development Institute to estimate model`s response to turbidity and vertical mixing schemes in the Yellow Sea. Simulations with M-Y vertical mixing scheme produced relatively stronger vertical mixing and warmer bottom temperature than the observation. KPP scheme produced weaker vertical mixing and did not well reproduce tidal mixing front along the coast. However, KPP scheme keeps bottom temperature closer to the observation. Consequently, numerical ocean circulation simulations with M-Y vertical mixing scheme tends to produce well mixed vertical temperature structure and that with KPP vertical mixing scheme tends to make stratified vertical temperature structure. When Jerlov water type is higher, sea surface temperature is high and sea bottom temperature is low because downward shortwave radiation is almost absorbed near the sea surface.
Effective analysis of Iris Images for Iris Recognition System

This paper proposes an iris recognition algorithm based on iris images. It consists of five major steps i.e., iris acquisition, localization, normalization, feature extraction and matching. The inner pupil boundary is localized using Circular Hough Transformation. The technique performs better in the case of occlusions and images muddled by artifacts such as shadows and noise. The outer iris boundary is detected by circular summation of intensity approach from the determined pupil center and radius. The localized iris image is transformed from Cartesian to polar co-ordinate system to handle different size, variation in illumination and pupil dilation. Corners in the transformed iris image are detected using covariance matrix of change in intensity along rows and columns. All detected corners are considered as features of the iris image. For recognition through iris, corners of both the iris images are detected and total number of codes that are matched between the two images are obtained. The two iris images belong to the same person if the number of matched corners is greater than some threshold value.
Alkynylation of Aldehydes and Ketones Using the Bu4NOH/H2O/DMSO Catalytic Composition: A Wide‐Scope Methodology

The Favorsky reaction of a wide range of aldehydes and ketones with alkynes has been implemented under mild conditions (5–20 °C). Using a Bu4NOH/H2O/DMSO catalytic system, propargylic alcohols are formed cleanly in 39–93 % (mostly 72–93 %) yields and with ca. 100 % selectivity. The method is suitable for aliphatic, aromatic, and heteroaromatic aldehydes and ketones, and for aliphatic, aromatic, and functionalized acetylenes. Thus, this represents the most general and efficient protocol to achieve the Favorsky reaction.