Miracle-dz/UniFAR-FTUs huggingface dataset:
Model-free controllers of a single-link smart material robot

In this paper, the controller design is investigated for a single-link smart material robot, which combines both the advantages of flexible robots and piezoelectric materials. To avoid any drawbacks resulting from model uncertainties and/or model truncations, a class of model-free controllers are proposed for the smart material robot system to achieve both tip regulation and residual vibration suppression. In contrast to traditional model-based methods, the controllers presented are derived from the basic energy-work relationship, which is independent of the system dynamics. Furthermore, the controllers are easily implementable because all the signals can be chosen readily measurable. Simulations are carried out to show the effectiveness of the presented approach.
Application of maximum entropy principle to software failure prediction

Predicting failures from software input is still a tough issue. Two models, namely the surface model and structure model, are presented in this paper to predict failure by applying the maximum entropy principle. The surface model forecasts a failure from the statistical co-occurrence between input and failure, while the structure model does from the statistical cause-effect between fault and failure. To evaluate the models, precision is applied and 17 testing experiments are conducted on 5 programs. Based on the experiments, the surface model and structure model get an average precision of 0.876 and 0.858, respectively
Transformation into Meaningful Chunks by Dividing or Connecting Utterance Units

The utterance units that serve as input to speech translation and/or spoken dialogue systems that handle spontaneous speech are not always sentences.However, the processing units of language translation are sentences.Since we do not have enough knowledge about the sentences of spoken languages,we use the term"meaningful chunks"instead of sentences.First,using conventionally interpreted dialogue data,we show that utterance units sometimes need to be divided into several meaningful chunks,and sometimes need to be connected to make up a single meaningful chunk.Next,we propose a method of transforming from utterance units to meaningful chunks based on pause information and the N-gram of fine-grained part-of-speech subcategories.We have conducted experiments and have confirmed that our method yields good results.
Towards adaptive perception in autonomous robots using second-order recurrent networks

In this paper a higher-order recurrent connectionist architecture is used for learning adaptive behaviour in an autonomous robot. This architecture consists of two sub-networks in a master-slave relationship: a function network for the coupling between sensory inputs and motor outputs, and a context network, which dynamically adapts the sensory input weights in order to allow a flexible, context-dependent mapping from percepts to actions. The capabilities of this architecture are demonstrated in a number of action selection experiments with a simulated Khepera robot, and it is argued that the general approach of generically dividing the overall control task between sequentially cascaded context and function learning offers a powerful mechanism for autonomous long- and short-term adaptation of behaviour.
Distributed system management treatments: toward the self-managing

In this paper, we present structuring concepts for distributed system management and a resulting distribution support tool kit. In order to deal with the complexity of the distribution environment management, an object model has been developed describing the behavior of the system objects and the necessary mechanisms for the control of this behavior. A computational model is used to define the treatments for the management information. Based on these models, the different tool kit management functions are designed and implemented.
Libera-te: Edutainment Project on Liberal Studies

This article discusses the design methodology and the 4 Is of the edutainment application development, namely instructional design, interface design, information design, and interaction design of Libera-te, which is an edutainment application designed for the education of Liberal Studies based on interactive detective game perspective. The outcome of this project is evaluated by semantic differential approach.
Improving Language Models in Speech-Based Human-Machine Interaction

This work focuses on speech-based human-machine interaction. Specifically, a Spoken Dialogue System (SDS) that could be integrated into a robot is considered. Since Automatic Speech Recognition is one of the most sensitive tasks that must be confronted in such systems, the goal of this work is to improve the results obtained by this specific module. In order to do so, a hierarchical Language Model (LM) is considered. Different series of experiments were carried out using the proposed models over different corpora and tasks. The results obtained show that these models provide greater accuracy in the recognition task. Additionally, the influence of the Acoustic Modelling (AM) in the improvement percentage of the Language Models has also been explored. Finally the use of hierarchical Language Models in a language understanding task has been successfully employed, as shown in an additional series of experiments.
Policy-based QoS architecture in the IP multimedia subsystem of UMTS

The third-generation mobile system, or Universal Mobile Telecommunication System, will offer IP-based multimedia applications and services with end-to-end QoS guarantee. The key part providing these services is the policy-based QoS control architecture in the IP multimedia subsystem (IMS). In this tutorial we describe the network entities involved in the policy-based QoS architecture, their interactions to realize guaranteed QoS in the UMTS network, and the communication interfaces employed in the interactions.
Analogue weighted median filter based on cellular neural network for standard video signal processing

A VLSI implementation of an analogue weighted median filter based on Cellular Neural Network (CNN) paradigm for standard video signal processing is described in this paper. This filter consists of feedforward nonlinear template B operating within the window of 3 by 3 pixels around the central pixel being filtered. The feedforward nonlinear coefficients are realized using a programmable nonlinear coupler circuits. Basic weighted median filter blocks and chip layout are presented. Technology applied for this implementation is CMOS AMS 0.8/spl mu/m CYE.
Context-Aware Optimization Algorithm of RFID Event Detection

Event detection over RFID event streams is one of the most important applications of RFID based monitoring systems. Event detection over data stream extracts meaningful complex patterns from high volume, fast speed and real time raw event streams. This research aims to detect RFID event patterns by introducing context information to reduce event instances effectively. Context of an RFID application is modeled and merged into event query optimization framework. Context information is further applied into evaluation of pattern detection. Experimental results show that context information can optimize complex event detection process both on CPU time and memory consumption.
Metandi: Meta-analysis of Diagnostic Accuracy Using Hierarchical Logistic Regression

Meta-analysis of diagnostic test accuracy presents many challenges. Even in the simplest case, when the data are summarized by a 2 x 2 table from each study, a statistically rigorous analysis requires hierarchical (multilevel) models that respect the binomial data structure, such as hierarchical logistic regression. We present a Stata package, metandi, to facilitate the fitting of such models in Stata. The commands display the results in two alternative parameterizations and produce a customizable plot. metandi requires either Stata 10 or above (which has the new command xtmelogit), or Stata 8.2 or above with gllamm installed.
EM-based semi-blind estimation of time-varying channels

OFDM systems traditionally perform channel coefficients estimation relying on known training sequences. However in wireless systems, performance and mobility can be further enhanced either by operating semi-blind channel estimation refinement or variation tracking between reference symbols. In this paper we propose an EM-based channel tracking method using a simple order 1 AR modelling of the channel variations. The proposed approach is applicable to both single carrier and OFDM systems. Simulation results presented in the context of 5 GHz WLAN show that this new algorithm allows us to reach the performance of a receiver having a perfect knowledge of the channel coefficients even in the mobile environment (3 m/s).
Isolated word intonation recognition using hidden Markov models

A method is described for recognition of intonation patterns based on discrete distribution hidden Markov models (HMMs) and vector quantization techniques. Fundamental frequency and energy features, were used to determine the best combination of feature processing and quantization techniques for recognition of statement, question, command, calling, and continuation intonation patterns in isolated words. A recognition accuracy of 89% was achieved for the best-case speaker- and word-independent performance. Recognition performance of human listeners on a 100-word subset yielded 77% accuracy, compared to 83% using HMMs on the same subset. <<ETX>>
Evaluation of a commercial impedance spectroscopy instrument: inaccuracies and their corrections

The accuracy of the Xitron bio-impedance spectrum analyzer was evaluated over a measurement range of 56 to 626 ohms and using lead resistances from 50 to 600 ohms to simulate electrode impedance (Z). Phase errors without lead resistances were up to 14.9/spl deg/. Lead impedance could add up to an additional 4/spl deg/ error. Although errors of this size could cause serious problems, it appears that by using low impedance electrodes and software corrections of the data significant improvement can be obtained.
A Graphical Approach for Assessing Optimal Operating Conditions in Robust Design

Abstract The determination of optimal operating conditions in robust parameter design often involves the use of an objective function such as the mean squared error of the response along with graphical overlays of the estimated process mean and variance functions. Existing graphical methods in robust design have limitations when more than two control factors are involved, since visualizing the entire control design space becomes difficult with many control factors. Here we present a new graphical technique for assessing optimal operating conditions which is based upon the estimated proportion of items within specification limits. The new plots offer users the ability to compare competing sets of optimal operating conditions, to assess possible assumption violations regarding the distribution of the noise factors, and the opportunity to observe the shape of the response distribution as it relates to stated specification limits. An example from manufacturing serves as the basis for illustration.
The effectiveness of HRM and KM in innovation performance: a literature review and research agenda

The purpose of this study is to develop a conceptual framework to examine the relationships among human resource management (HRM), knowledge management (KM) and innovation performance (IP). The background of HRM, KM and theory of IP serve as a starting-point in developing the conceptual framework. This paper fills the gap in the current literature by providing senior managers of manufacturing or service industry who want to evaluate an innovation performance competency. Senior managers can concentrate their efforts on practices that show the most promising influence for establishing competitive innovation performance capabilities by developing a deeper comprehension of the association between HRM and KM.
A Method of Measuring User Influence in MicroBlog

Microblog is a popular social networking service. In order to analyze the user behavior in microblog, a method is proposed in this paper to measure the influence of users. By analyzing the data set collected from Sina microblog, it is worth noting that the number of the followers and friends obey power-law distribution. In addition, the users display a weak correlation in their number of followers and friends, and show a strong correlation in their retweet influence and mention influence. Besides, we propose a method to model user influence by taking consideration the factors including the number of followers and friends as well as the scale of the network. The experiment results indicate that the model can calculate the value of user influence and effectively reflect the influence of users. The proposed method and result have important implications in understanding human behaviors and provide new insights for microblog’s study.
A Streaming Computation Framework for the Cell Processor

The Cell architecture is a heterogeneous, distributed-memory multicore architecture that features a novel on-chip communication network. Stream programs are particularly well-suited for execution on Cell. This thesis implements a runtime library on Cell specifically designed to support streaming applications and streaming language compilers. The runtime library abstracts the details of Cell's communication network and provides facilities that simplify the task of scheduling stream actors. The library is designed in the context of the StreamIt programming language. This library is used to implement a dynamic scheduling framework. The programmability of high-level schedulers with and without the library is analyzed. We observe that the library does not add significant overhead, provides a number of useful features for schedulers, and greatly simplifies the code required to implement schedulers. Thesis Supervisor: Saman Amarasinghe Title: Associate Professor
Simultaneous design of structure and control systems for two-degree-of-freedom-controller

This paper discusses simultaneous design of plant structure and a controller with two-degree-of-freedom. We propose a new criterion for a feedforward property with a concrete design procedure of controllers, which is suitable for a simultaneous design problem. Numerical example demonstrates the effectiveness of the proposed criterion. It also shows that feedforward and feedback properties are not independent in case of simultaneous design.
Fault detecting experiments for sequential circuits

This paper describes the design of experimental procedures for determining whether or not a sequential switching circuit is operating in accordance with a given state-table description. These procedures are particularly easy to apply when the given state table is reduced, strongly-connected, and has a distinguishing sequence, and when the actual circuit has no more states than the given table. They can also be extended to cover more general cases, although the resulting experiments are more cumbersome.
Land cover classification in Western Loess Plateau of China with MODIS imagery

Land cover classification represents one of the most fundamental applications of remote sensing, and is widely used to estimate carbon stocks, hydrological parameters and biogeochemical models. Land cover classification using multi-temporal and multi-spectral remote sensing observation data has been an effective technique. This paper adopts a multi-dimension classification method to classify the land cover in Western Loess Plateau of China using EOS/MODIS remote sensing image and some ancillary data. The imagery used in this study were 30-day composite normalized difference vegetation index (NDVI) over the year 2003 obtained by the maximum value composite (MVC) technique, and the ancillary data included phonological calendar, agricultural knowledge and existing land cover dataset. Discrete Fourier transform (DFT) as a signal decomposition method can extract periodic responses through expressing a NDVI time series curve with the sum of an additive term and a series of cosine waves and it has been used in land cover classification. Therefore the discrete Fourier transform was applied to the NDVI data set on a per-pixel basis for the whole study areas and images of the additive, amplitude and phase angle for the first two harmonics were produced on a per-pixel basis for each pixel in the NDVI dataset. After integrating the multi-dimension information, the land cover in Western Loess Plateau of China is finally classified using ISODATA unsupervised classification algorithm. At last, the accuracy of the result was evaluated with existing land cover dataset. The tentative result shows good correlations with existing digital land cover map, though the small overestimation or underestimation are recognized in several categories.
Understanding Microchip's CAN Module Bit Timing

The Controller Area Network (CAN) protocol is an asynchronous serial bus with Non-Return to Zero (NRZ) bit coding designed for fast, robust communications in harsh environments, such as automotive and industrial applications. The CAN protocol allows the user to program the bit rate, the sample point of the bit, and the number of times the bit is sampled. With these features, the network can be optimized for a given application.
Editing Knowledge in Large Mathematical Corpora. A case study with Semantic LaTeX (sTeX)

Before we can get the whole potential of employing computers in the process of managing mathematical `knowledge', we have to convert informal knowledge into machine-oriented representations. How exactly to support this process so that it becomes as effortless as possible is one of the main unsolved problems of Mathematical Knowledge Management. Two independent projects in formalization of mathematical content showed that many of the time consuming tasks could be significantly reduced if adequate tool support were available. It was also established that similar tasks are typical for object oriented languages and that they are to a large extent solved by Integrated Development Environments (IDE). This thesis starts by analyzing the opportunities where formalization process can benefit from software support. A list of research questions is compiled along with a set of software requirements which are then used for developing a new IDE for the semantic \TeX{} (\stex{}) format. The result of the current research is that, indeed, IDEs can be very useful in the process of formalization and presents a set of best practices for implementing such IDEs.
Absolute Navigation Information Estimation for Micro Planetary Rovers

This paper provides algorithms to estimate absolute navigation information, e.g., absolute attitude and position, by using low power, weight and volume Microelectromechanical Systems-type (MEMS) sensors that are suitable for micro planetary rovers. Planetary rovers appear to be easily navigable robots due to their extreme slow speed and rotation but, unfortunately, the sensor suites available for terrestrial robots are not always available for planetary rover navigation. This makes them difficult to navigate in a completely unexplored, harsh and complex environment. Whereas the relative attitude and position can be tracked in a similar way as for ground robots, absolute navigation information, unlike in terrestrial applications, is difficult to obtain for a remote celestial body, such as Mars or the Moon. In this paper, an algorithm called the EASI algorithm (Estimation of Attitude using Sun sensor and Inclinometer) is presented to estimate the absolute attitude using a MEMS-type sun sensor and inclinometer, only. Moreover, the output of the EASI algorithm is fused with MEMS gyros to produce more accurate and reliable attitude estimates. An absolute position estimation algorithm has also been presented based on these on-board sensors. Experimental results demonstrate the viability of the proposed algorithms and the sensor suite for low-cost and low-weight micro planetary rovers.
Population ageing and future tax burdens : An integrated micro-macro analysis of possible taxation policy changes

Rising dependency ratio after 2015 and subsequent increased government expenditures on pensions and health and social care, represent a basic challenge for Norway as for OECD-countries, provided that the present welfare state schemes are maintained at the present level. The purpose of this paper is to analyse how sensitive the required surge in the tax burden, implemented as an increase in the payroll tax rate, is to endogenous labour supply responses to the growth in after tax real wages and nonlabour income. The paper also examines to what extent the necessary increase in the tax burden and labour supply is significantly affected when a hypothetical flat tax rate on most income categories is chosen as the endogenous tax instrument instead of the pay-roll tax rate. The analysis is based on a modelling framework that integrates a detailed micro-econometric simulation model of labour supply with a Computable General Equilibrium (CGE) model. The micro model captures a wide range of heterogeneity in individual labour supply behaviour and an accurate description of the personal tax system. The CGE-model provides a detailed account of the endogenous changes in other tax bases and government expenditures.
Zonepow: a Novel Approach to Power control in Ad-Hoc Networks

This paper presents an attempt to address the problem of power control at the network layer by proposing a novel protocol called ZONEPOW for Ad Hoc Mobile Networks. The aim of ZONEPOW is primarily to increase the overall traffic carrying capacity of the network. The solution to the power control problem is based on (i) the search for a common power level limited to discrete zones that proactively guarantees connectivity within the zone, and (ii) an appropriate, reactive forwarding procedure for any out of zone traffic. Being a hybrid power control protocol ZONEPOW is suited for ad-hoc networks and it is particularly attractive in networks where communication between hosts is mostly localized and communication across zones is comparatively rare.
ReRAM‐Based Neuromorphic Computing

Artificial neural networks are intended to solve complex machine-learning tasks by using massive parallel data processing in a similar way as in biological neural systems. A recent approach to mimic biology is to emulate the basic processing elements directly in hardware. ReRAM devices are considered key elements to realize highly scalable, and low-power neuromorphic systems consist of using a hybrid analog–digital circuit approach. The specific properties of ReRAM devices that make those devices highly useful as artificial synapse are highlighted in detail. Especially, the multilevel capability of ReRAM devices enables the implementation of learning rules such as spike-timing-dependent plasticity (STDP). The scaling perspectives of ReRAM-based neuromorphic architectures are elaborated on, revealing a scaling potential below 10 nm. Finally, several neuromorphic applications using ReRAM architectures are reviewed and an evaluation of the future perspectives is given.
Market Segmentation: Conceptual and Methodological Foundations

Introduction: the historical development segmentation bases segmentation methods tools for market segmentation. Part 1 Segmentation methodology: clustering methods mixture models mixture-regression models mixture-unfolding models profiling segments dynamic segmentation. Part 2 Special topics in market segmentation: joint segmentation market segmentation with tailored interviewing model-based segmentation using simultaneous and structural equation models. Part 3 Applied market segmentation: general observable bases - geo-demographics general unobservable bases - values and lifestyles product-specific observable bases - response-based segmentation product-specific unobservable bases - conjoint analysis conclusions and directions for future research.