Miracle-dz/UniFAR-FTUs huggingface dataset:
Ανάλυση σχεδίαση και ανάπτυξη ειδικών ασύρματων δικτύων βασισμένων σε ενσωματωμένα συστήματα

The research objective of this thesis is a complete solution so as to support the implementation of telecommunication applications in wireless networks that operate without the presence of fixed infrastructure. Such a network, cited as Mobile Ad-hoc Network (MANET) in international literature, is a self-organized, auto-configured wireless network with multi-hop routing paths where its structure is modified dynamically due to node mobility and topological changes. In order to meet the above requirements a new communication framework for MANET networks is presented, which consists of the adaptation of communication-related energy reduction techniques, development of a specialized routing protocol at the network layer, modifications of the TCP protocol at the transport layer and the implementation of a novel network management protocol. The performance of the proposed protocol stack is satisfactory in dynamic network topologies with increased node mobility, whereas the node energy consumption remains in low levels. The presented algorithms and protocols are designed and implemented considering the constraints and requirements of an embedded system, while their performance and reliability is proved theoretically and by simulation results.
A single microphone noise reduction algorithm based on the detection and reconstruction of spectro-temporal features

Animals throughout the animal kingdom excel at extracting individual sounds from competing background sounds, yet current state-of-the-art signal processing algorithms struggle to process speech in the presence of even modest background noise. Recent psychophysical experiments in humans and electrophysiological recordings in animal models suggest that the brain is adapted to process sounds within the restricted domain of spectro-temporal modulations found in natural sounds. Here, we describe a novel single microphone noise reduction algorithm called spectro-temporal detection–reconstruction (STDR) that relies on an artificial neural network trained to detect, extract and reconstruct the spectro-temporal features found in speech. STDR can significantly reduce the level of the background noise while preserving the foreground speech quality and improving estimates of speech intelligibility. In addition, by leveraging the strong temporal correlations present in speech, the STDR algorithm can also operate on predictions of upcoming speech features, retaining similar performance levels while minimizing inherent throughput delays. STDR performs better than a competing state-of-the-art algorithm for a wide range of signal-to-noise ratios and has the potential for real-time applications such as hearing aids and automatic speech recognition.
Interleave Division Multiple Access Scheme: An Overview

This paper outlines a survey on interleave-division multiple-access (IDMA) technique which exploits the interleaving as only means of user separation instead of by different signatures as in a conventional code-division multiple-access (CDMA) scheme. IDMA scheme can achieve near single user performance in situations with very large numbers of users while maintaining very low receiver complexity. This technique inherits many advantages from CDMA, such as diversity against fading and mitigation of the worst-case user interference problem. This paper presents an overview of IDMA and discusses the main features and areas of application as the future air interface access schemes for IMT-2000/UMTS.
Influences of Linguistic and Non-Linguistic Factors in the Processing of American Sign Language: Evidence from Handshape Monitoring *

0. Introduction Signed languages used in Deaf communities are naturally occurring human languages. Despite the differences in language form, signed languages have formal linguistic properties like those found in spoken languages (Stokoe 1960, Klima and Bellugi 1979, Sandler and Lillo-Martin 2006). However, only a few psycholinguistic studies of on-line processing in signed languages exist (for reviews of psycholinguistic studies of ASL see Emmorey 2002, Corina and Knapp 2006), and only a subset of these have directly addressed lexical recognition. Studies of lexical recognition in signed languages have provided evidence for well-known psycholinguistic properties such as lexicality, usage frequency, and semantic and form-based context effects (Corina and Emmorey 1993, Mayberry and Witcher 2005, Dye and Shih 2006, Carreiras et al. 2008). However, a fuller explication of the processes by which a gestural-manual sign form is mapped to a meaningful lexical representation remains to be determined. A prominent issue is to determine whether the processes involved in sign recognition are driven by factors that are common to human action recognition in general or entail specialized linguistic processing. The present experiment investigates perceptual, motor and linguistic factors of sign recognition in the context of a sub-lexical monitoring task.
An empirical investigation of the impact of computer support on group development and decision-making performance

A Group Decision Support System (GDSS) is ". an interactive, computer-based system which facilitates solution of unstructured problems by a set of decision makers working together as a group" (DeSanctis and Gallupe, 1985). This study examined the impact of using a GDSS on group development and decision making performance in a controlled laboratory environment. This study specifically examined the following four questions: (1) Does computer support affect the performance of decision making groups? (2) Does computer support affect the development of decision making groups? (3) Are the patterns of performance different between computer supported and manual groups? (4) Are the patterns of development different between computer supported and manual groups? These questions were studied using a two-factorial repeated measures research design. This is the first GDSS study to use a repeated measures design. The specific variables of interest in this study are outlined: (1) Independent variables. Computer support and group history. (2) Dependent variables. Group development and decision making performance. (3) Controlled variables. Task type, task complexity, task structure, group size, individual differences (through randomization), physical environment, time taken to make decisions, and facilitation. The study used 28 groups of 5 members each (140 subjects) to examine the above four questions. Half the groups received computer support and half did not. Each group met once a week for four weeks. Results showed that there were significant differences in group development and decision making performance between groups that received computer support and groups that did not.
Optimization of the Fading MIMO Broadcast Channel: Capacity and Fairness Perspectives

Multiple input multiple output (MIMO) systems are now a proven area in current and future telecommunications research. MIMO wireless channels, in which both the transmitter and receiver have multiple antennas, have been shown to provide high bandwidth efficiency. In this thesis, we cover MIMO communications technology with a focus on cellular systems and the MIMO broadcast channel (MIMO-BC). Our development of techniques and analysis for the MIMO-BC starts with a study of single user MIMO systems. One such single user technique is that of antenna selection. In this thesis, we discuss various flavours of antenna selection, with the focus on powerful, yet straightforward, norm-based algorithms. These algorithms are analyzed and the results of this analysis produce a powerful and flexible power scaling factor. This power scaling factor can be used to model the gains of norm-based antenna selection via a single signal-to-noise ratio (SNR)-based parameter. This provides a powerful tool for engineers interested in quickly seeing the effects of antenna selection on their systems. A novel low complexity power allocation scheme follows on from the selection algorithms. Named “Poor Man’s Waterfilling” (PMWF), this scheme can provide significant gains in low SNR systems with very little extra complexity compared to selection alone. We then compare a variety of algorithms for the MIMO-BC, ranging from selection to beamforming, to the optimal, yet complex, iterative waterfilling (ITWF) solution. In this thesis we show that certain algorithms perform better i in different scenarios, based on whether there is shadow fading or not. A power scaling factor analysis is also performed on these systems. In the cases where the user’s link gains are widely varying, such as when shadowing and distance effects are present, user fairness is impaired when optimal and near optimal throughput occurs. This leads to a key problem in the MIMO-BC, the balance between user fairness and throughput performance. In an attempt to find a suitable balance between these two factors, we modify the ITWF algorithm by both introducing extra constraints and also by using a novel utility function approach. Both these methods prove to increase user fairness with only minor loss in throughput over the optimal systems. The introduction of MIMO systems to the cellular domain has been hampered by the effects of interference between the cells. In this thesis we move MIMO to the cellular domain, addressing the interference using two different methods. We first use power control, where the transmit power of the base station is controlled to optimize the overall system throughput. This leads to promising results using low complexity methods. Our second method is a novel method of collaboration between base stations. This collaboration transforms neighbouring cell sectors into macro-cells and this results in substantial increases in performance.
Multi-type relational clustering approaches : current state-of-the-art and new directions

The proliferation of multi-type relational datasets in a number of important real-world applications and the limitations resulting from the transformation of such datasets to fit propositional data mining approaches have led to the emergence of the discipline of multi-type relational data mining. Clustering is an important unsupervised learning task aimed at discovering structure inherent in data. In this paper, we survey the state-of-the-art in the field of relational clustering, providing a taxonomy of approaches and review some of the most representative algorithms within each category. We also present DIVA, our general framework for multi-type relational clustering, which combines the use of Representative Objects with multi-phase clustering in a bid to provide flexibility, efficiency and effectiveness in clustering relational datasets. Theoretical analysis and experimental results prove that our approach is more effective and efficient than a number of other algorithms proposed in literature.
Analysis of the Effect of Clustering the Training Data in Naive Bayes Classifier for Anomaly Network Intrusion Detection

This paper presents the analysis of the effect of clustering the training data and test data in classification efficiency of Naive Bayes classifier. KDD cup 99 benchmark dataset is used in this research. The training set is clustered using k means clustering algorithm into 5 clusters. Then 8800 samples are taken from the clusters to form the training and test set. The results are compared with that of two Naive Bayes classifiers trained on random sampled data containing 8800 and 17600 instances respectively. The main contribution of this paper is that it is empirically proved that the training set derived from clusters generated by k-means clustering algorithm improves the classification efficiency of the Naive Bayes classifier. The results show the accuracy of the Naive Bayes classifier trained with clustered instances is 94.4% while that of normal instances are 85.41% and 89.26%.
A control – theoretic approach for adaptive streaming over HTTP

Video applications account for a large percentage of Internet traffic, and their utilization tends to grow. In this scenario, multimedia providers are required to deliver video and/or audio content in a seamless manner, regardless of network conditions and client device capabilities. However, current commercial services are not efficient enough, and the academic proposals do not take full advantage of their potential. To this end, we propose a control-theoretic adaptive streaming over HTTP system, which adapts the video quality to the network conditions in a video session. With this system, we aim at delivering video with the highest Quality of Experience possible, while leveraging the available network resources. Our system is located at the client side, and comprises a control system and a state machine. Together, they select the video level that is requested to the server, a standard HTTP server, which sends video chunks as the client requests them. To select the control system and state machine that best fit our requirements, we perform several experiments with different parameter values combinations. Our parameter selection is accomplished in two steps, the last one resulting in the combination that best fits our needs. Then, we compare our system performance to an existent system in the academia. Our experimental results show the benefits of providing more aggressive decisions on the video quality selection. However, they also show that such aggressiveness may, sometimes, compromise the system performance. Overall, though, our system outperforms the baseline of our experiments.
Making Privacy a Fundamental Component of Web Resources

We present a social network inspired and access control list based sharing model for web resources. We have specified it as an extension for OpenSocial 1.0 and implemented a proof of concept in Orkut as well as a mobile social photo sharing application using it. The paper explains important design decisions and how the model can be leveraged to make privacy a core component and enabler for sharing resources on the web and beyond using capabilities of mobile devices.
InSAR coherence estimation for temporal analysis and phase unwrapping applications

In SAR interferometry, coherence between data acquisitions is potential information on the accuracy of interferograms. In this paper, a new coherence estimation approach is investigated. Our study is based on a binary mixture modelisation of coherence images. A Bayesian criteria is so used to discriminate between the two statistical mixed laws. It is shown that empirical coherence is biased and depends on the number of pixels averaged. The results of this paper can be applied to the estimation of the interferogram noise in interferometric SAR.
X-ray Digital Tomosynthesis Imaging — Comparison of Reconstruction Algorithms in Terms of a Reduction in the Exposure Dose for Arthroplasty

Aims The purpose of this review was (1) to identify indications for volumetric X-ray digital tomosynthesis by using a conventional reconstruction technique [the filtered back-projection (FBP) algorithm] and modern reconstruction techniques [the maximum likelihood expectation maximization (MLEM) and simultaneous iterative reconstruction techniques (SIRT)] and (2) to compare the conventional and modern reconstruction techniques in terms of a reduction in the exposure dose. Review The methods included the following: (1) an overview and analysis of the characteristics of the FBP, MLEM, and SIRT algorithms; (2) an overview of the properties of phantom imaging for arthroplasty when imaging overlying structures and the effect of those properties on various artifacts in images; and (3) a review of each method regarding exposure reductions. Summary In the phantom study, the MLEM and SIRT techniques can suppress streak artifacts; therefore, they warrant further evaluation in comparison with FBP. With the FBP technique, the exposure dose may be decreased to half of the reproducibility for a reconstructed prosthesis phantom image. The results show the characteristics of each technique that need to be considered in clinical practice (better suppression of streak artifacts: MLEM and SIRT; better reproducibility: FBP). In addition, under‐ standing the advantages of each reconstruction technique during digital tomosyn‐ thesis imaging will improve diagnostic accuracy in clinical applications.
100Base-T/IEEE 802.12/packet switching

Three LAN technologies look set to satisfy the ever-increasing demand for LAN bandwidth. Two of these technologies are 100 Mb/s shared-medium LANs: 100Base-T (aka IEEE 802.3 Fast Ethernet) and IEEE 802.12 (aka 100VG-AnyLAN or 100VG). The third technology is packet switching, which is really an extension of existing LAN bridge technology, but offers excellent performance gains at very low cost. The authors describe the three technologies and provide a comparison between the two 100 Mb/s LANs. Also presented are results that compare the measured performance of 100 Mb/s shared-medium LANs with switched LANs.
Stream Processing To Solve Image Search by Similarity

The classic use of Stream Processing platforms enables working with data in real time, which allows you to generate data analysis quickly attending to a decisionmaking process. However, you can use these platforms for other applications such as indexing and subsequent use of similarity search objects in a database. The images can be displayed on a metric space, which has features that allow rules to discard a not similar image quickly without making costly computations. This paper presents the use of a Stream Processing platform to index images generated by different users. For this, it is necessary to represent these images by vectors containing different MPGE-7 features. This paper shows a Stream Processing platform using its processing elements (PEs) in parallel to speed up the operations involved in the index construction.
A 32Mb–4b/cell analog flash memory supporting variable density with 3V–only supply and serial I/O

This 8M cells Flash EEPROM using the Analog Flash technology is able to store up to 32Mbit of digital data (4bit/cell). The fully integrated memory chip has a 3V-only power supply and a dual standard configurable serial I/O interface (SPI and I2C) to allow 6+1 pin packaging suited for small form factor removable memory card. The analog flash programming is managed by an embedded microcontroller. The chip is fabricated in a standard 3V 0. 5µm common-ground NOR Flash-EEPROM process (2 polys, 3 metals) with channel hot electron programming and Fowler-Nordheim tunnelling erasing. The cell is 1.7×1. 9µm2, the chip area is 13.5×6.1mm2.
Alignment Between Principal And Teacher Beliefs About Technology Use

This paper explores the link between principals' and teachers' pedagogical beliefs regarding the benefits of integrating online learning technologies into language teaching and learning contexts. Principals who have the leadership ability to initiate a clear vision to carry out the pedagogical requirements for technological change in teaching and learning approaches can direct the use of technology to enhance the school learning environment . The paper reports on the initial phase of data collection for a PhD thesis at an Australian University. Two surveys were developed and implemented for this study in order to determine principals' and teachers' existing pedagogical beliefs regarding online learning technologies. Participants included 67 principals and 82 Arabic language teachers across secondary schools in Jeddah city in Saudi Arabia. The results present a strong alignment between principal and teacher beliefs in that both indicate positive constructivist beliefs including improvement of teachers' and students' research skills, the promotion of students learning both inside and outside school, and the belief that online learning technologies convert teacher-centred teaching approaches to student-centred teaching approaches. Additionally, the study shows that principals' beliefs were always stronger than teachers' beliefs.
Stabilization and output tracking for underactuated mechanical systems with inequality state constraints

The paper presents a method to determine the feasibility of stabilization to an equilibrium manifold or exact output tracking for underactuated mechanical systems that are subject to inequality state constraints. Even for minimum phase systems, internal dynamics may evolve in an unacceptable way and has to be confined within certain limits. Such restrictions arise in deformable object manipulation tasks. It is shown that the problem of output tracking under inequality state constraints is equivalent to output tracking with bounded input. The paper provides sufficient conditions for exact output tracking and stabilization to an equilibrium manifold which guarantee that the internal dynamics is bounded with adjustable bounds.
An Analysis of the Public Data for Making the Ambient Intelligent Service

In current society, the digital era that makes enormous amount of data, and the diversified city, the smart space, which has characteristics of creating, collecting and representing data, is appeared. After 2012, in the social media environment called hyper-connected society with wide-spread smart phone, people started to get interested in public data and big data by generalized mobile device and SNS. At first, development of forming platform of data was focused, but now, many different idea from diverse area have been suggested about data analysis and usage to visualize the space intellectualization service. To focus on the visualization process to increase the usage of this public data for ordinary people more than specialized people, this research grasps the present condition of open data and public data service from the current public data portal and considers the applicability of them. As the result of research, the analysis and application of data to ordinary people decrease the use of paper documents, and this research will help to develop the application which is fast and accurate about individual behavior and demand to utilize public data service in intellectual space.
Educational Uses of Digital Storytelling all around the World

The purpose of this research study was to determine how educators, students and others around the world are using digital storytelling to support the educational process. This study provides a general framework about educational uses of digital storytelling in different countries and describes the different perceptions of people in different cultures about this technology. An online survey was used to collect responses from a group of educators, students and others in educational settings to determine how they are using digital storytelling for educational purposes. The results show the current situation of educational uses of digital storytelling around the world and explore some of the benefits and challenges educators face in implementing digital storytelling in their
Image reconstruction using the Viterbi algorithm

Many systems in widespread use concentrate on the imaging of binary objects, e.g., the archival storage of text documents on microfilm or the facsimile transmission of text. Due to the imperfect nature of such systems, the binary image is unavoidably corrupted by blur and noise to form a grey-scale image. We present a technique to reverse this degradation which maps the binary object reconstruction problem into a Viterbi state-trellis. We assign states of the trellis to possible outcomes of the reconstruction estimate and search the trellis in the usual optimal fashion. Our method yields superior estimates of the original binary object over a wide range of signal-to-noise ratios (SNR) when compared with conventional Wiener filter (WF) estimates. For moderate blur and SNR levels, the estimates produced approach the maximum likelihood (ML) bound on estimation performance.