Miracle-dz/UniFAR-FTUs huggingface dataset:
A passive-RFID based indoor navigation system for visually impaired people

An indoor navigation system based on passive RFID is described. It allows the user to move autonomously resorting to a grid of RFID tags for localization and simple procedures to determine the position and to plan the route. Ad-hoc designed RFID tags permit to achieve reliable and accurate position estimation while a proximity algorithm allows real-time localization. Many details of the localization infrastructure are shown together with some experimental results.
A Dynamic Balancing Approach for a Quadruped Robot Supported by Diagonal Legs

For legged robots, the most important task is to keep balance. This paper proposes a new balance control approach. To simplify the control complexity, first, LQR (linear quadratic regulator) control was used to obtain stable state feedback for the model. Then, the 6-DOF model was stabilized by dividing the whole robot into three separate parts. After that, VMC (virtual model control) was used to change the configuration of the joints. The simulation results showed that the proposed method allowed the quadruped robot to walk stably, even when certain types of disturbance were exerted on the models. In the simulation model, to mimic real conditions, noise was added to the sensors; the algorithm was then verified as still suitable for the quadruped robot.
Comparative Study between Robust Control of Robotic Manipulators by Static and Dynamic Neural Networks

A comparative study between static and dynamic neural networks for robotic systems control is considered. So, two approaches of neural robot control were selected, exposed, and compared. One uses a static neural network; the other uses a dynamic neural network. Both compensate the nonlinear modeling and uncertainties of robotic systems. The first approach is direct; it approximates the nonlinearities and uncertainties by a static neural network. The second approach is indirect; it uses a dynamic neural network for the identification of the robot state. The neural network weight tuning algorithms, for the two approaches, are developed based on Lyapunov theory. Simulation results show that the system response, equipped by dynamic neural network controller, has better tracking performance, has faster response time, and is more reliable to face disturbances and robotic uncertainties.
Message Filtering Techniques for On-Line Social Networks: A Survey

Today On-line Social Networks (OSNs) is one of the most popular interactive medium to share and communicate among the internet user. But there is one fundamental issue is to give users the ability to control the messages posted on their own private space to avoid that unwanted content is displayed. Up to now OSNs provide little support to this requirement. This papers give comprehensive review on various techniques of flexible rule-based system, and a Machine Learning based soft classifier. Whereas flexible rule-based system, helps to customize the filtering criteria. Machine Learning based soft classifier for automatically labeling messages in support of content-based filtering.
Fast algorithms for retiming large digital circuits

The increasing complexity of VLSI systems and shrinking time to market requirements demand good optimization tools capable of handling large circuits. Retiming is a powerful transformation that preserves functionality, and can be used to optimize sequential circuits for a wide range of objective functions by judiciously relocating the memory elements. Leiserson and Saxe, who introduced the concept, presented algorithms for period optimization (minperiod retiming) and area optimization (minarea retiming). The ASTRA algorithm proposed an alternative view of retiming using the equivalence between retiming and clock skew optimization. The first part of this thesis defines the relationship between the Leiserson-Saxe and the ASTRA approaches and utilizes it for efficient minarea retiming of large circuits. The new algorithm, Minaret, uses the same linear program formulation as the Leiserson-Saxe approach. The underlying philosophy of the ASTRA approach is incorporated to reduce the number of variables and constraints in this linear program. This allows minarea retiming of circuits with over 56,000 gates in under fifteen minutes. The movement of flip-flops in control logic changes the state encoding of finite state machines, requiring the preservation of initial (reset) states. In the next part of this work the problem of minimizing the number of flip-flops in control logic subject to a specified clock period and with the guarantee of an equivalent initial state, is formulated as a mixed integer linear program. Bounds on the retiming variables are used to guarantee an equivalent initial state in the retimed circuit. These bounds lead to a simple method for calculating an equivalent initial state for the retimed circuit. The transparent nature of level sensitive latches enables level-clocked circuits to operate faster and require less area. However, this transparency makes the operation of level-clocked circuits very complex, and optimization of level-clocked circuits is a difficult task. This thesis also presents efficient algorithms for retiming large level-clocked circuits. The relationship between retiming and clock skew optimization for level-clocked circuits is defined and utilized to develop efficient retiming algorithms for period and area optimization. Using these algorithms a circuit with 56,000 gates could be retimed for minimum period in under twenty seconds and for minimum area in under 1.5 hours.
Learning language-independent sentence representations for multi-lingual, multi-document summarization

This paper presents an extension of a denoising auto-encoder to learn language-independent representations of parallel multilingual sentences. Each sentence from one language is represented using language dependent distributed representations. The input of the auto-encoder is then constituted of a concatenation of the distributed representations corresponding to the vector representations of translations of the same sentence in different languages. We show the effectiveness of the learnt representation for extractive multidocument summarization, using a simple cosine measure that estimates the similarity between vectors of sentences found by the auto-encoder and the vector representation of a generic query represented in the same learnt space. The top ranked sentences are then selected to generate the summary. Compared to other classical sentence representations, we demonstrate the effectiveness of our approach on the TAC 2011 MultiLing collection and show that learning language-independent representations of sentences that are translations one from another helps to significantly improve performance with respect to Rouge-SU4 measure.
A linear induction motor control system for magnetically levitated carrier system

An electromagnetic suspension system has been developed that cuts off electric power-collecting devices from a magnetically levitated vehicle. This system makes it possible to transport materials with no mechanical contact at all between the vehicle and ground facilities. A control system for linear induction motors used in the magnetically levitated carrier system is described. The system was developed to transport materials in an environment which must be kept free from even microscopic dust motes and trivial noise. The linear induction motor control method used for positioning a vehicle at the station is described along with several kinds of switches without any mechanical motion in the ground facilities. A supervisory control system for the magnetically levitated carrier system is also discussed. >
Towards an Object-Oriented Analysis and Design Method for Hardware/Software Systems. A Case Study.

For several years now, the Digital Information Systems Group of Eindhoven University of Technology is doing research on structured specification, analysis and design methods for embedded systems. Part of the research focuses on an object-oriented method for hardware/software systems. This analysis and design method-SHE-consists of models and a framework to guide modeling activities. Research is assisted with practical input from this masters project. In this masters project existing ideas and theories are tried out and new ones are applied to a real-world problem and evaluated. Research to currently available object-oriented approaches indicated OMT-Object-oriented Modeling Technique-of Rumbaugh et.al. [14] as a relative suitable method. OMT is cited by many prominants in the field of system design and is taught on several universities. OMT models show three different but related views of a system: object, dynamic and functional view. OMT's design framework is divided in four phases: analysis, system design, object design and implementation. As part of this masters project, OMT is applied to an elevator problem. The elevator problem contains considerable dynamic behavior and physical and abstract hierarchies. Applying OMT to the elevator problem showed that it was not as suitable for analysis and design of hardware/software systems as expected. Experiences with OMT were used to build a requirements list for a method for software/hardware engineering. The new method SHESoftware/Hardware Engineering-was checked with the requirements when it became available in a first form. This thesis describes SHE as far as it has evolved. SHE is applied to the elevator problem as well. SHE showed to tackle several problems that were encountered with OMT, but has some problems of its own as well. These problems are analyzed and described in this thesis. They may serve as input for discussions and future research. SHE seems to be a very promising object-oriented method for analysis and design of complex hardware/software systems.
Simulation of Internet Connectivity for Mobile Ad Hoc Networks in Network Simulator-2

Ad Hoc networking allows portable devices to establish communication independent of a central infrastructure. The issue of routing in a mobile ad hoc network becomes a challenging task since the mobile nodes are free to move randomly. There are several ad hoc routing protocols, such as Ad hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR), Optimized Link State Routing (OLSR) and Zone Routing Protocol (ZRP), which propose solution for routing within a mobile ad hoc network. In this paper the ad hoc routing protocol AODV is used and modified to examine the interconnection between a mobile ad hoc network and the Internet, for this purpose Network Simulator 2 (NS-2) has been used. Moreover, three proposed approaches for gateway discovery are simulated.
Design and Implementation of Chinese Information Filtering System

With the development of the network information, information processing is vital in all aspects. Information filtering is more important research aspect. Especially Chinese information filtering is urgent affairs. According to research of the domestic and abroad, in the article vector space method and hyB+ tree index method is combined to filter text. Experimental results show that, this method is feasible.
Deterministic and stochastic channel models implemented in a physical layer simulator for Car-to-X communications

Abstract. This paper presents a physical (PHY) layer simulator of the IEEE 802.11p standard for Wireless Access in Vehicular Environments (WAVE). This simulator allows the emulation of data transmission via different radio channels as well as the analysis of the resulting system behavior. The PHY layer simulator is part of an integrated simulation platform including a traffic model to generate realistic mobility of vehicles and a 3D ray-optical model to calculate the multipath propagation channel between transmitter and receiver. Besides deterministic channel modeling by means of ray-optical modeling, the simulator can also be used with stochastic channel models of typical vehicular scenarios. With the aid of this PHY layer simulator and the integrated channel models, the resulting performance of the system in terms of bit and packet error rates of different receiver designs can be analyzed in order to achieve a robust data transmission.
GPS error and its effects on movement analysis

Global Navigation Satellite Systems (GNSS), such as the Global Positioning System (GPS), are among the most important sensors for movement analysis. GPS is widely used to record the trajectories of vehicles, animals and human beings. However, all GPS movement data are affected by both measurement and interpolation error. In this article we show that measurement error causes a systematic bias in distances recorded with a GPS: the distance between two points recorded with a GPS is -- on average -- bigger than the true distance between these points. This systematic `overestimation of distance' becomes relevant if the influence of interpolation error can be neglected, which is the case for movement sampled at high frequencies. We provide a mathematical explanation of this phenomenon and we illustrate that it functionally depends on the autocorrelation of GPS measurement error ($C$). We argue that $C$ can be interpreted as a quality measure for movement data recorded with a GPS. If there is strong autocorrelation any two consecutive position estimates have very similar error. This error cancels out when average speed, distance or direction are calculated along the trajectory. Based on our theoretical findings we introduce a novel approach to determine $C$ in real-world GPS movement data sampled at high frequencies. We apply our approach to a set of pedestrian and a set of car trajectories. We find that the measurement error in the data is strongly spatially and temporally autocorrelated and give a quality estimate of the data. Finally, we want to emphasize that all our findings are not limited to GPS alone. The systematic bias and all its implications are bound to occur in any movement data collected with absolute positioning if interpolation error can be neglected.
The Learner's Voice: a Focus on the e-learner Experience

This paper outlines work in progress on a national JISC research project on the learner experience of e-learning. The project is named LEX, a contraction of the long name but also a reference to the importance of using the learner's own words in the analysis. The project covers a wide range of post-16 learners including adult, community and work-based learners as well as FE and HE learners, distributed widely across the UK. We describe the development, evolution and implementation of the research methodology, and how we tackled practical problems of reaching such a diverse learner group. We go on to outline three case studies which illustrate how learners describe their approaches to fitting learning into their lives, to accomplishing e-learning tasks, their strategies to overcome problems, and their expectations and experiences of e-learning across a range of educational contexts and technology use. The paper does not present findings or research outcomes, however tentative, since we are only at the start of the analytical phase. The case studies do however indicate some of the issues that learners have raised in the research.
From Index Locorum to Citation Network: an Approach to the Automatic Extraction of Canonical References and its Applications to the Study of Classical Texts

My research focusses on the automatic extraction of canonical references from publications in Classics. Such references are the standard way of citing classical texts and are found in great numbers throughout monographs, journal articles and commentaries. In chapters 1 and 2 I argue for the importance of canonical citations and for the need to capture them automatically. Their importance and function is to signal text passages that are studied and discussed, often in relation to one another as can be seen in parallel passages found in modern commentaries. Scholars in the field have long been exploiting this kind of information by manually creating indexes of cited passages, the so-called indices locorum. However, the challenge we now face is find new ways of indexing and retrieving information contained in the growing volume of digital archives and libraries. Chapters 3 and 4 look at how this problem can be tackled by translating the extraction of canonical citations into a computationally solvable problem. The approach I developed consists of treating the extraction of such citations as a problem of named entity extraction. This problem can be solved with some degree of accuracy by applying and adapting methods of Natural Language Processing. In this part of the dissertation I discuss the implementation of this approach as a working prototype and an evaluation of its performance. Once canonical references have been extracted from texts, the web of relations between documents that they create can be represented as a network. This network can then be searched, manipulated, visualised and analysed in various ways. In chapter 5 I focus specifically on how this network can be leveraged to search through bodies of secondary literature. Finally in chapter 6 I discuss how my work opens up new research perspectives in terms of visualisation, analysis and the application of such automatically extracted citation networks.
Written Mixed Language Variety in Multilingual South African Media: Intellectualisation of African Languages (isiZulu)

The South African Constitution embraces multilingualism and makes commitments aimed at promoting the use and development of indigenous languages. This paper investigates the extent to which different parts of government and other influential structures of society use the media in their attempt to make multilingualism a reality. Data were collected from the internet, billboards, newspapers and pamphlets, leading to qualitative analysis of written English and isiZulu code switching. The findings illustrate creative ways in which written switches occur at clause boundary, in the form of tags and within sentences. Such an approach is considered a step in the right direction towards the intellectualisation of African languages as it has the potential to contribute to the promotion of multilingualism.
Handbook of software reliability engineering

Technical foundations introduction software reliability and system reliability the operational profile software reliability modelling survey model evaluation and recalibration techniques practices and experiences best current practice of SRE software reliability measurement experience measurement-based analysis of software reliability software fault and failure classification techniques trend analysis in validation and maintenance software reliability and field data analysis software reliability process assessment emerging techniques software reliability prediction metrics software reliability and testing fault-tolerant SRE software reliability using fault trees software reliability process simulation neural networks and software reliability. Appendices: software reliability tools software failure data set repository.
Automatic sleep stage classification based on sparse deep belief net and combination of multiple classifiers

This paper presents an automatic sleep stage method combining a sparse deep belief net and combination of multiple classifiers for electroencephalogram, electrooculogram and electromyogram. The sparse deep belief net was applied to extract features from these signals automatically, and the combination of multiple classifiers, utilizing the extracted features, assigned each 30-s epoch to one of the five possible sleep stages. More importantly, we proposed a new voting principle based on classification entropy to enhance the classification performance further by harnessing the complementary information provided by the individual classifier. Differently from existing methods, our method used unsupervised feature learning to extract features automatically from raw sleep data and classification based on the learned features. The results of automatic and manual scorings were compared on an epoch-by-epoch basis. The accuracies for wake, S1, S2, SWS and REM were 98.49%, 80.05%, 91.2%, 98.22% and 95.31%, respectively, and the total accuracy of sleep stage was 91.31%. The results demonstrated that the sparse deep belief net was an efficient feature extraction method for sleep data, and the combination of multiple classifiers based on classification entropy performed well on sleep stages.
Medical Image Segmentation Algorithms using Deformable Models: A Review

Abstract Image segmentation is an important step in image processing. Major advances in the field of medical imaging have provided physicians with powerful and non-invasive techniques to probe the structure, function, and pathology of the human body. This information requires significant innovations in all aspect of image processing. Manual tracing of object boundaries generally suffers from poor reproducibility of results and it is also tedious and time consuming. Further, manual segmentations are often restricted to two dimensional (2D) slice-wise processing, which often suffers from inconsistency across segmented slices. Quantitative analysis of medical images requires reproducible, accurate, and efficient segmentation methods. By making use of medical image segmentation approaches accurate, repeatable, quantitative data must be efficiently extracted in order to support the spectrum of biomedical investigations and clinical activities, from diagnosis to radiotherapy as well as to surgery. Purpose of this review is to identify the set of methods that have been used for medical image segmentation over the past three decades, and to provide an opportunity to view the transitions that have occurred as this research area has developed. In this paper, various approaches of medical image segmentation, available algorithms for deformable models are reviewed and their advantages, disadvantages, and limitations are discussed.
SIR-based power control in a CDMA system

Power control in the use of direct-sequence code-division multiple-access (CDMA) techniques is addressed. Previous performance analyses of CDMA wireless mobile and personal communications have assumed the ability of power control to equalize the absolute signal powers of users received at each base station. A more practical, although analytically more complicated, uplink power controls technique that uses measurements of the received signal-to-interference ratio. (SIR) instead is studied. A combination of discrete-event link simulation and analysis of the obtained SIR statistics is used to explore the behavior of a CDMA system using SIR-based power control and to obtain performance estimates for such a system under various operating assumptions. The overall results indicate that power control based on SIR has the potential for somewhat higher system performance than power control based on absolute signal strength. <<ETX>>
Scalable Ion Trap Architecture for Universal Quantum Computation by Collisions

We propose a scalable ion trap architecture for universal quantum computation, which is composed of an array of ion traps with one ion confined in each trap. The neighboring traps are designed capable of merging into one single trap. The universal two-qubit $\sqrt{SWAP}$ gate is realized by direct collision of two neighboring ions in the merged trap, which induces an effective spin-spin interaction between two ions. We find that the collision-induced spin-spin interaction decreases with the third power of two ions' trapping distance. Even with a $200\ \mu m$ trapping distance between atomic ions in Paul traps, it is still possible to realize a two-qubit gate operation with speed in $0.1\ kHz$ regime. The speed can be further increased up into $0.1\ MHz$ regime using electrons with $10\ mm$ trapping distance in Penning traps.