Miracle-dz/UniFAR-FTUs huggingface dataset:
A Programmable Compensation Circuit for System-on-Chip Application

This paper presents a new programmable compensation circuit (PCC) for a System-on-Chip (SoC). The PCC is integrated with O.18-J.1m BiCMOS SiGe technology. It consists of RF Design-for-Testability (DFT) circuit, Resistor Array Bank (RAB) and digital signal processor (DSP). To verify performance of the PCC we built a 5-GHz low noise amplifier (LNA) with an on-chip RAB using the same technology. Proposed circuit helps it to provide DC output voltages, hence, making the RF system chain automatic. It automatically adjusts performance of an LNA with the processor in the SoC when it goes out of the normal range of operation. The PCC also compensates abnormal operation due to the unusual PVT (Process, Voltage and Thermal) variations in RF circuits.
RELM Testing Center

We describe the organizational setup and the set of rules developed to test earthquake likelihood models of the Regional Earthquake Likelihood Models (RELM) initiative. Truly prospective testing for a multitude of forecasts in an unbiased and reproducible way requires a set of rules to be obeyed by each model as well as a cyber-infrastructure: the testing center. These rules encompass free parameters, e.g., the testing area and grid, forecast periods, magnitude ranges, earthquake catalogs provided to the models and used for testing, and declustering. A combination of these free parameters defines a class of models. Within RELM we distinguish between five-year models, one-year models, and one-day models, which issue their forecast for the respective periods. While five-year models provide their forecasts as numbers, one-year and one-day models must be installed in the testing center together with their source code to enable yearly or daily forecast generation. Only with installed models can results be reproduced at any time. The declared RELM goal of testing a multitude of forecast models in a prospective (forward-looking) sense is, to our knowledge, unique in seismology. It is the first time that a group of modelers agree to submit their models to a common, community-agreed test against future seismicity. We believe that this is an important milestone for forecasting- and prediction-related research in seismology because it offers an opportunity to overcome past shortcomings and deadlocks. The primary goal of the common testing is not to find the ultimate winner but to advance our current physical understanding and/or the statistical description of the way earthquakes occur. Ultimately this will lead in small steps (or, possibly, giant leaps) to better forecast models. Prospective testing of models in seismology is rare but a few laudable examples exist (Kagan and Jackson 1995, Evison and Rhoades 1999, Rong et …
An introductory study on deep neural networks for high resolution aerial images

The aim of this paper is to discuss the use deep neural networks (DNNs) for road detection from high-resolution aerial images. DNNs are a powerful mathematical model for information processing that has been proven to be effective to accomplish several complex tasks including coding and classification of image data. The DNN with its many trainable weights allows taking a large context into account that is essential for the difficult problem of detect roads from aerial image. Furthermore it allows to progressively combining lower-level features or concepts into more abstract and higher-level representations. Thus, we believe useful to discuss possible applications DNNs for roads detection in the present work.
Demystifying Mixed Methods Research Design: A Review of the Literature.

Accepted: 09.07. 2013 Mixed methods research evolved in response to the observed limitations of both quantitative and qualitative designs and is a more complex method. The purpose of this paper was to examine mixed methods research in an attempt to demystify the design thereby allowing those less familiar with its design an opportunity to utilize it in future research. A review of the literature revealed that it has been gaining acceptance among researchers, researchers have begun using mixed methods research, it has the potential to offer more robust research, researchers are encouraged to outline clearly the reason(s) for using mixed methods design, and it is a more complex research design. Mixed methods research has become a valid alternative to either quantitative or qualitative research designs. It offers richer insights into the phenomenon being studied and allows the capture of information that might be missed by utilizing only one research design, enhances the body of knowledge, and generates more questions of interest for future studies that can handle a wider range of research questions because the researcher is not limited to one research design. Researchers will need to be knowledgeable in quantitative, qualitative, and mixed methods designs.
Photosensitivity and Scanning of Silicon Image Detector Arrays

The first part of this paper deals with the basic photodiode unit, its principle of operation, and the factors that affect the photodiode and array sensitivity. Experimental results of the planar Si p/sup +/-n photodiode are presented. Design and layout considerations including the integration time are also given. The second part of this paper deals with scanning of photodiode arrays. Detail design considerations are given for a static MOST ring counter. A dynamic shift register with clocked loads for integral scanning is also considered. Design considerations of a realized photosensing matrix with integral scanning are given. Finally, scanning problems of large arrays are briefly considered.
Nonlinear control with flux observer for a singularly perturbed induction motor

This paper provides a feedback composite control with nonlinear sliding-mode flux observer for an induction motor using singular perturbation methods. This feedback is composed of slow and fast parts. The authors prove the stability of the closed-loop system with observer. This decomposition simplifies the control scheme which is computed separately on the basis of two subsystems (slow and fast). The control of an induction motor is considered as a case study. <<ETX>>
A Corpus-based Approach to Philological Issues

The aim of this work is the application of techniques developed in the domain of corpus linguistics to a collection of ancient Greek texts, taking into account not only the canonical text established by modern editors, but also the variant readings recorded in the critical apparatus or in the repertories of conjectures. The dissertation is divided in three connected parts: construction, mapping and analysis of the corpus. The first part is devoted to corpus construction and it is focused on the techniques to improve the OCR accuracy on classical critical editions. This task is challenging because critical editions are multilingual, the set of characters to recognize is wide and the quality of last centuries paper is variable. Three OCR engines are applied to the same texts and a Bayesian classifier, joint to a specific spell-checker, evaluates the most probable output. It is demonstrated that the improvement is significative and, in the best cases, it is more than 3%. The second part is devoted to the alignment of the contents extracted from critical apparatus and repertories of conjectures to the reference text. TOGLIERE A parser has been developed to classify the chunks of information (verse number, Greek word sequences, textual operation, scholar that suggested the conjecture). Alignment algorithms used to find the precise position of the conjecture in its context are illustrated in detail. The third part is devoted to the study of the semantic spaces of ancient Greek. The chapter is focused on the specificity of the corpus, that is morphologically complex, literary (both poetry and prose) and diachronical (from VIII century B.C. to XV century A.D.). The word senses in documents belonging to different genres are explored, and the diachronical change of meaning is observed. Finally, a couple of meaningful conjectures extracted in the first part is analysed, evaluating the most interesting reciprocal relations in the semantic space.
Continuous Mandarin speech recognition for Chinese language with large vocabulary based on segmental probability model

The author presents a study of large-vocabulary continuous Mandarin speech recognition based on a segmental probability model (SPM) approach. SPM was found to be very suitable for recognition of isolated Mandarin syllables especially considering the monosyllabic structure of Chinese language. To extend the application of the model to continuous Mandarin speech recognition, a concatenated syllable matching (CSM) algorithm in place of the conventional Viterbi search algorithm is first introduced. Also, to utilise the available training material efficiently, a training procedure is proposed to re-estimate the SPM parameters using the maximum a posteriori (MAP) algorithm. A few special techniques integrating acoustic and linguistic knowledge are developed further to improve the performance step by step. Preliminary experimental results show that the final achievable rate is as high as 91.62%, which indicates a 18.48% error rate reduction and more than three times faster than the well studied subsyllable-based CHMM.
Knowledge asset value spiral: linking knowledge assets to company's performance

In today's economy, more and more organisations are realising that they must explicitly manage their knowledge resources to acquire and maintain their competitive advantage. For this reason, managers need to better understand how they can identify and evaluate existing knowledge resources within an organisation and how to manage them in order to achieve competitive advantage. Many organisations frequently embark on Knowledge Management (KM) initiatives without a clear idea of what business benefits they could expect. This is mainly due to lack of understanding of the links between KM and company's business performance. Understanding these links supports the validation of KM investment and can help explain what knowledge should be managed within an organisation to achieve performance improvement. It appears from literature analysis, that there is not straightforward link between KM and company's performance but rather a complex relationship. In such a prospect, the main research question that has inspired this study is: ‘How can an organisation plan and evaluate a KM initiative designed to improve company's performances? ’ On the base of this question, this paper provides a framework—the Knowledge Assets Value Spiral (KAVS)—which focuses on how to apply KM initiatives in a step by step process to achieve company's performance improvements. The framework provides guidelines to plan, implement and evaluate KM initiatives that are linked to performance objectives. It is illustrated how organisations can identify their key knowledge assets as well as their interdependencies and better understand their role in causal dynamics in company's value delivering. The application of the Analytic Hierarchy Process (AHP) method is proposed and a case example describing the application of the framework is illustrated. Copyright © 2006 John Wiley & Sons, Ltd.
A Hybrid Shape Optimization Approach for Complex Applications in Computational Fluid Dynamics

Abstract A shape optimization tool for complex applications in Computational Fluid Dynamics combines a RANSE solver, hybrid genetic algorithms and artificial neural networks. Two approaches for mesh updating are presented, relying on linear and torsional spring analogies or spatial deformation laws. The influence of turbulence models during the optimization loop is analyzed for hull shape optimization at full scale.
Iterative decoding of GMSK modulated convolutional code with multiple differential detection

A novel receiver architecture for the serial concatenation system of a convolutional code and a Gaussian minimum shift keying (GMSK) modulator is proposed. A maximum a posteriori probability algorithm for the multiple differential detection (MDD) of GMSK signals, which accepts a priori information and provides a soft-output for the subsequent convolutions decoder, is derived. The soft-output of the decoder is fed back to the MDD receiver for further improvement, thus resulting in an iterative decoding structure.
Erlang capacity of CDMA systems with adaptive arrays

We present a technique to calculate the number of subscribers which can be supported in a CDMA system while using adaptive arrays. Results presented using this technique show the tremendous capacity increases possible while using adaptive arrays. The advantages of adaptive arrays over sector antennas and beamformers is quantified by considering Erlang capacity and spatial clustering of subscribers. This study has practical implications in wireless local loop systems and the results presented clearly highlight the benefits of using a dynamic spatial processor like adaptive arrays as compared with the fixed patterns used in sector antennas. We also consider the effects of high powered multimedia users in a voice-dominated system and results presented illustrate that by using smart antennas, it is possible to maintain a sizeable voice capacity while admitting a small number of multimedia users.
Knowledge Template Based Multi-perspective Car Recognition Algorithm

In order to solve the problem due to the vehicle-oriented society such as traffic jam or traffic accident, intelligent transportation system(ITS) is raised and become scientist's research focus, with the purpose of giving people better and safer driving condition and assistance. The core of intelligent transport system is the vehicle recognition and detection, and it's the prerequisites for other related problems. Many existing vehicle recognition algorithms are aiming at one specific direction perspective, mostly front/back and side view. To make the algorithm more robust, our paper raised a vehicle recognition algorithm for oblique vehicles while also do research on front/back and side ones. The algorithm is designed based on the common knowledge of the car, such as shape, structure and so on. The experimental results of many car images show that our method has fine accuracy in car recognition.
English Language Learners' Reading Self-Efficacy and Achievement Using 1:1 Mobile Learning Devices

Handheld technology devices allow users to be mobile and access the Internet, personal data, and third-party content applications in many different environments at the users' convenience. The explosion of these mobile learning devices around the globe has led adults to value them for communication, productivity, and learning. Outside of the school setting, many adolescents and children have access to, or own mobile devices. The use of these individual devices by children on a daily basis in schools is a relatively new phenomenon, with just four percent of elementary students doing so in classrooms in 2010(Gray, Thomas & Lewis, 2010). This mixed methods study researched a one-to-one implementation of percent devices in fourth- and fifth-grade elementary classrooms. The focus was to explore the mobile learning device's relationship to English language learners' reading achievement, to English language learners' self-efficacy in reading, and to explore the benefits and limitations of the device's daily use, as perceived by the students. The hypothesis was that the practice of reading and related literacy activities with mobile learning devices would augment English learners' vicarious learning experiences, and thereby effect student cognitive engagement, reading self-efficacy, and reading academic achievement. This study used validated surveys and assessments to measure students' beliefs about reading and their knowledge of reading. Additionally, English language learner interview data were also collected and analyzed to uncover perceived benefits and limitations of utilizing 1:1 mobile learning devices daily for literacy activities. Analysis of the data revealed significantly elevated levels of self-efficacy in reading for the experimental group with 1:1 handheld technology, while academic gains in reading for the experimental and control groups were statistically similar. Students in the experimental group described a virtually-enhanced socio-cultural context for communicating and learning with the handheld technology. Implications for practice, policy, and future research are discussed
Fault-tolerant design of analogic CNN templates and algorithms-Part I: The binary output case

This paper addresses the issue of designing a class of fault-tolerant cellular neural network (CNN) templates that, combined with CNN analogic algorithms, work correctly and reliably on given CNN universal machine (CNN-UM) chips. In particular, a generic method for finding nonpropagating binary-output CNN templates is proposed. This method is based on measurements of actual CNN-UM chips and combines adaptive optimization and decomposition of theoretically ideal CNN templates in order to correct the erroneous behavior of actual CNN-UM chips, which is mainly caused by imperfections introduced during fabrication. More specifically, the entire array of cells in a CNN-UM chip is modeled by a single feed-forward virtual cell whose optimal parameters are found by a simple and effective gradient-based method. In the case of binary input-output uncoupled templates (or Boolean operators), a systematic template decomposition method is applied whenever optimization fails to find a correctly working CNN template for all possible combinations of local 3/spl times/3 binary input patterns. The resulting templates are finally combined, yielding a simple CNN analogic algorithm. Examples are presented for both binary- and analog-input operators, using two concrete stored-program CNN-UM chips to demonstrate the effectiveness of the proposed method, whose advantages and limitations are also discussed.
FPGA implementation of sparse matrix algorithm for information retrieval

Information text data retrieval requires a tremendous amount of processing time because of the size of the data and the complexity of information retrieval algorithms. In this paper the solution to this problem is proposed via hardware supported information retrieval algorithms. Reconfigurable computing may adopt frequent hardware modifications through its tailorable hardware and exploits parallelism for a given application through reconfigurable and flexible hardware units. The degree of the parallelism can be tuned for data. In this work we implemented standard BLAS (basic linear algebra subprogram) sparse matrix algorithm named Compressed Sparse Row (CSR) that is showed to be more efficient in terms of storage space requirement and query-processing timing over the other sparse matrix algorithms for information retrieval application. Although inverted index algorithm is treated as the de facto standard for information retrieval for years, an alternative approach to store the index of text collection in a sparse matrix structure gains more attention. This approach performs query processing using sparse matrix-vector multiplication and due to parallelization achieves a substantial efficiency over the sequential inverted index. The parallel implementations of information retrieval kernel are presented in this work targeting the Virtex II Field Programmable Gate Arrays (FPGAs) board from Xilinx. A recent development in scientific applications is the use of FPGA to achieve high performance results. Computational results are compared to implementations on other platforms. The design achieves a high level of parallelism for the overall function while retaining highly optimised hardware within processing unit.
3.3V-5V compatible I/O circuit without thick gate oxide

A novel 3.3V-5V compatible [/O circuit is proposed and measured to be effective from the stand-point of reliability and speed. It can endure 5V input from the external chips, although it is made only with thin gate oxide designed for use under 3.3V supply voltage. A test chip is fabricated and the measured propagation delay time, tpLH and tpHL, of an output buffer are 311s and 2.5ns, respectively. Output high level, VOH, is 3.3V.
Evaluating LexCSD-a weakly-supervised method on improved semanticallyannotated corpus in a large scale experiment.

Word Sense Disambiguation in text is still a difficult problem as the best supervised methods require laborious and costly manual preparation of training data. On the other hand, the unsupervised methods express significantly lower accuracy and produce results that are not satisfying for many application. Recently, an algorithm based on weakly-supervised learning for WSD called LexicographerControlled Semi-automatic Sense Disambiguation (LexCSD) was proposed. The method is based on clustering of text snippets including words in focus. For each cluster we find a core which is labelled with a word sense by a human and is used to produce a classifier. Classifiers, constructed for each word separately, are applied to text. The goal of this work is to evaluate LexCSD trained on large amount of untagged text. A performed comparison showed that the approach is better than most frequent sense baseline in most cases and in some cases beat the supervised equivalents. For the need of experiment semantically annotated corpus was improved in terms of coverage of sense inventory and annotations.
Equational code generation: implementing categorical data types for data parallelism

The Bird-Meertens formalism is a second-order functional style of data parallel programming in which a fixed set of second order functions for each type is generated by a categorical construction. The mathematical structure underlying such categorical data types is important for software development by transformation. We show that it can also be used at compile time to allow code generation in an equational way. <<ETX>>
Continuous optical automatic speech recognition by lipreading

We describe a continuous optical automatic speech recognizer (OASR) that uses optical information from the oral-cavity shadow of a speaker. The system achieves a 25.3 percent recognition on sentences having a perplexity of 150 without using any syntactic, semantic, acoustic, or contextual guides. We introduce 13, mostly dynamic, oral-cavity features used for optical recognition, present phones that appear optically similar (visemes) for our speaker, and present the recognition results for our hidden Markov models (HMMs) using visemes, trisemes, and generalized trisemes. We conclude that future research is warranted for optical recognition, especially when combined with other input modalities. <<ETX>>
TCP over ATM: simulation model and performance results

While the transmission control protocol (TCP) generally provides robust performance across many network environments, several researchers have identified the poor end-to-end performance achieved by TCP on asynchronous transfer mode (ATM) networks. The performance problems arise for several reasons: (1) a size mismatch between TCP segments and ATM cells; (2) the simple protocols used for segmentation and reassembly in ATM, and for retransmission in TCP; (3) specific "optimizations" in TCP, made primarily for low-bandwidth Internet environments which do not work well in high speed ATM networks; (4) features (or misfeatures) in most TCP protocol implementations; and (5) subtle interactions between all of these factors. This paper describes a simulation model that we have constructed to study TCP performance on ATM networks, as well as a set of simulation experiments conducted using the model. The TCP model is detailed enough to recreate many of the performance problems identified by other researchers, as well as to evaluate potential solutions to the performance problems. The TCP model adds to the traffic modeling toolkit available in our existing ATM network simulator, and enables the further study of performance issues in TCP over ATM.