Miracle-dz/UniFAR-FTUs huggingface dataset:
Aerial Recovery of Micro Air Vehicles: Orbit Estimation and Tracking

Aerial Recovery of Micro Air Vehicles: Orbit Estimation and Tracking Daniel C. Carlson Department of Mechanical Engineering Master of Science Aerial recovery of autonomous micro air vehicles (MAVs) presents many unique challenges due to the difference in size and speed of the recovery vehicle and MAV. This thesis presents algorithms to enable an autonomous MAV to estimate the orbit of a recovery vehicle and track the orbit until the final docking phase. Methods for estimating ellipses that are rotated out of the x− y plane are developed and demonstrated through simulation. These algorithms are shown to be robust to noise and stable numerically. Parameter update methods that are computationally inexpensive, such as recursive least squares and Kalman filtering, are discussed and simulated. A discussion is given of orbit tracking algorithms for circular orbits, and these methods are expanded to include elliptical orbits. These algorithms enable the MAV to track the recovery vehicle’s orbit, based on a vector field approach. The tracking algorithms are divided into lateral and longitudinal controllers that allow for tracking of inclined orbits. Finally, the hardware and software setup for live flight tests is discussed. Flight test results are given that validate the functionality of the orbit estimation and orbit tracking algorithms.
A MAC Protocol Considering Traffic Loads Information For a Clustered Wireless Sensor Networks

In this paper, we proposed an efficient TDMA scheduling algorithm for a clustered Wireless Sensor Network. Since some previous algorithms used unnecessary idle period and schedule period in each frame. It became an overhead that might consume unexpected energy and delay data transmission. To solve this problem, a dynamic scheduling algorithm according to the number of member nodes and node traffic load within a cluster was suggested. Our proposed DS-MAC(Dynamic Scheduling MAC) could save energy and reduce transmission delay Then DS-MAC was analyzed mathematically to compare with the previous algorithms.
Recycling Bits in LZ77-Based Compression

We present a technique that exploits the multiplicity of the ways a text may be encoded using an LZ77-based compression method. With such methods, repeated parts of the text are encoded as length-distance pairs that refer to previously seen text. In general, given the maximum length of a repeated part, there may be more than one distance at which there is a copy of the repeated part. The compressor is free to select any of these distances since the decompressor is able to recover the same text anyway. The mere act of choosing one of these distances can be used to convey information to the decompressor. We present the details of our technique. We have integrated our technique in a high performance compressor in order to make measurements. The experiments show that our technique significantly improves compression rates on many files of the Calgary corpus.
Correlation and calibration effects on MIMO capacity performance

MIMO wireless systems are studied in this paper with particular emphasis on the achieved performance in terms of achieved capacity, in different operational environments. First, the definition of a MIMO system is presented along with the performance advantages. Then, Shannon's extended capacity formula is discussed and a simplified expression is derived by applying linear transformations. The paper concentrates on the capacity of Rayleigh channels and then studies the case where the signals are submitted to fading correlation, furthermore a study on the effects of calibration errors regarding capacity is presented.
Détection temporelle de saillance dynamique dans des vidéos par apprentissage profond

Le probleme etudie concerne l'analyse de la saillance dynamique dans des videos. Plus precisement, nous cherchons a chaque instant a determiner si une image peut etre classee comme saillante ou non selon des informations liees au mouvement dans l'image. Une image sera detectee comme saillante si elle contient des elements dont le mouvement se demarque de son contexte spatio-temporel. L'approche proposee traite les cas ou la camera est en mouvement et utilise l'apprentissage profond. Plusieurs variantes en sont proposees et comparees. La detection temporelle est pertinente pour de nombreuses applications ou une alerte ou une attention particuliere doit etre declenchee en rapport avec le contenu dynamique des videos. Des experimentations sur une base de videos reelles variees montrent la tres bonne precision de classification obtenue.
Speech Processing, Recognition and Artificial Neural Networks: Proceedings of the 3rd International School on Neural Nets "Eduardo R. Caianiello"

Section 1 - Fundamentals of Speech Analysis and Perceptron. - Articulatory Constraints on Distinctive Features. - "Herr Muller vivra a Taranto con i suoi colleghi austriaci" Investigations on a fragment of Italian Phonology. - Acoustic Analysis and Perception of Classes of Sounds (vowels and consonants). - Speech and Voice Perception: Beyond Pattern Recognition. - Section 2 - Speech Processing. - Analysis in Automatic Recognition of Speech. - Speech Production and Perception Models and their Applications to Synthesis, Recognition, and Coding. - Section 3 - Stochastic Models for Speech. - Statistical Methods for Automatic Speech Recognition. - Statistical Modelling: from Speech Recognition to Text Translation. - Continuous Speech Recognition with Neural Networks: An Application to Railway Timetables Enquires. - Multi-Level Multi-Decision Model for Automatic Speech Recognition and Understanding. - Generative Models for Automatic Speech Recognition, Understanding and Synthesis. - Speech Modelling Virtual Laboratory. - Section 4 - Auditory and Neural Network Models for Speech. - Auditory Modeling and Neural Networks. - Neural Networks for Automatic Speech Recognition: a Review. - Preprocessing and Classification of English Stops Nasals and Fricatives. - Self-Organizing Feature Maps for Arabic Phonemes. - Section 5 - Task-Oriented Applications of Automatic Speech Recognition and Synthesis. - Towards Fully Automatic Speech Processing Techniques for Interactive Voice Servers. - Multi-modal Speech Synthesis with Applications. - Author Index.
KNOWLEDGE REPRESENTATION AND SOFTWARE SELECTION FOR EXPERT SYSTEMS DESIGN

A variety of techniques and methods for representing knowledge in the knowledge base of expert systems have been used. The authors examine the significance of the means of representing the knowledge base in the development of expert systems, with special reference to transportation engineering. The development, highlights, and shortcomings of each representation technique are discussed, and appropriate transportation engineering examples are given. Also presented are the results of an investigation of expert system tools and how they relate to different representation techniques.
Cluster Ensemble and Probabilistic Neural Network Modeling of Class Imbalance Learning in Software Defect Prediction

Machine learning techniques are frequent for the complicated task of predicting software defects. Often the prediction models fail to predict defects successfully as the between class imbalance increases in the training data. This paper conducts experiments on NASA KC1 dataset. Experiments were carried out on five different datasets with different class imbalance ratio, made from original KC1 dataset. Performance variations of each classifier were analyzed to detect software defects as the imbalance ratio increases for training datasets. Moreover different classifier’s performances were also investigated while trained with same datasets to find out the better one for each case. It is found that our proposed Cluster Ensemble based Probabilistic Neural Network approach has performed better than all other approaches including SMOTE based Probabilistic Neural Network for the given data sets. It is also found that incorporating Cluster Ensemble with SMOTE based Probabilistic Neural Network approach performs better than only SMOTE based Probabilistic Neural Network approach.
Security data visualization : graphical techniques for network analysis

Acknowledgments Introduction Chapter 1: An Overview of Information Visualization Chapter 2: The Beauty of Binary File Visualization Chapter 3: Port Scan Visualization Chapter 4: Vulnerability Assessment and Exploitation Chapter 5: One Night on My ISP Chapter 6: A Survey of Security Visualization Chapter 7: Firewall Log Visualization Chapter 8: Intrusion Detection Log Visualization Chapter 9: Attacking and Defending Visualization Systems Chapter 10: Creating a Security Visualization System Chapter 11: Unexplored Territory Chapter 12: Teaching Yourself
Machine learning approaches for neuron geometry extraction and synapse detection in electron microscopy images

In this thesis we investigate the whole image processing pipeline for neuronal geometry extraction and synapse detection in electron microscopy images. Advancements in automated sample preparation and image acquisition for electron microscopy enable recording of large data sets. This process is especially important for the eld of computational neuroanatomy and connectomics, as the analysis of neuronal connections requires imaging of large volumes with a resolution su cient for synapse detection. Manual processing of electron microscopy data is time-consuming and becoming the main bottleneck in gaining new insights into the functional structure of the brain. Automated processing of biological electron microscopy images is challenging due to the rich texture, low signal to noise ratio and the great variability of image characteristics depending on sample preparation and animal type. To enable quantitative evaluation of the data, the images are corrected against lens distortions, stitched, and aligned. Structures of interest are then segmented and grouped across serial sections to extract the 3d geometry. The proposed registration methods employ unsupervised approaches to identify artifact signals like non-linear distortions, cracks, or staining blurs. We demonstrate that identi cation of these signals leads to superior registration results compared to state-of-the-art methods. The distortion correction enables structure preserving mosaicing with sub-pixel precision. The non-linear distortion eld is estimated from overlapping image areas and does not require special calibration samples.
Chips Challenging Champions: Games, Computers and Artificial Intelligence

A. Introduction. Games, Computers and Artificial Intelligence (J. Schaeffer, H.J. van den Herik). B. Puzzles. Disjoint Pattern Database Heuristics (R.E. Korf, A. Felner). Sokoban: Enhancing General Single-Agent Search Methods Using Domain Knowledge (A. Junghanns, J. Schaeffer). A probabilistic Approach to Solving Crossword Puzzles (M.L. Littman, G.A. Keim, N. Shazeer). C. Two-Player Perfect-Information Games. Deep Blue (M. Campbell, A.J. Hoane Jr, F.-h. Hsu). Improving Heuristic Min-Max Search by Supervised Learning (M. Buro). A Hierarchical Approach to Computer hex (V. Anshelevich). Computer shogi (H. Iida, M. Sakuta, J. Rollason). Computer go (M. Mueller). D. Imperfect Information and Stochastic Games. Programming Backgammon Using Self-Teaching Neural Nets (G. Tesauro). The Challenge of Poker (D. Billings, A. Davidson, J. Schaeffer, D. Szafron). World-championship-caliber Scrabble (B. Sheppard). E. Solved Games. Games Solved: Now and in the future (H.J. van den Herik, J.W.H.M. Uiterwijk, J. van Rijswijck). Author Index.
Who Needs Controlled Vocabulary

Whenever they search a database, how do searchers decide whether to use a descriptor, a textword, or both ? Observation of a total of 281 real-life searches performed by 47 professional searchers shows that although some searchers preferred descriptors and others textwords, the decision about which type to use depended on each specific situation. These analyses revealed general rules that explain the selection of search terms.
Playful identity in game design and open-ended play

to the world, to other people and to themselves. This prompts questions about present-day mediations of identity. This book explores the notion of play as a heuristic lens to look at changing media practices and identity construction. Playful media culture is analyzed far beyond its apparent manifestation in computer games. The central argument of the book is that play and games nowadays are not only appropriate metaphors to capture post-modern human identities, but also the very means by which people reflexively construct their identity. Playful Identities presents academic research at the intersection of media theory, play and game studies, social sciences and philosophy. The book carves out a cross-disciplinary domain that connects the most recent insights from play and game studies, media research, and identity studies.
Practical Formal Software Engineering: Wanting the Software You Get

Practical Formal Software Engineering is a textbook aimed at final year undergraduate and graduate students, emphasizing formal methods in writing robust code quickly. Engineering is an informal process using formal logics as tools and components to obtain timely practical solutions. This book takes an engineering approach to illuminate the creation and verification of large software systems in which theorems and axioms are intuited as the formalism materializes through practice. Where other textbooks discuss business practices through generic project management techniques or detailed rigid logic systems, this book examines the interaction between code in a physical machine and the logic applied in creating the software. These elements create an informal and rigorous study of logic, algebra, and geometry through software. Assuming prior experience with C, C++, or Java programming languages, chapters introduce UML, OCL, and Z from scratch. Based around a theme of the construction of a game engine, extensive worked examples motivate readers to learn the languages through the technical side of software science.
A Survey of Collective Intelligence

This chapter presents the science of "COllective INtelligence" (COIN). A COIN is a large multi-agent systems where: i) the agents each run reinforcement learning (RL) algorithms; ii) there is little to no centralized communication or control; iii) there is a provided world utility function that, rates the possible histories of tile full system. Tile conventional approach to designing large distributed systems to optimize a world utility does not use agents running RL algorithms. Rather that approach begins with explicit modeling of the overall system's dynamics, followed by detailed hand-tuning of the interactions between the components to ensure that they "cooperate" as far as the world utility is concerned. This approach is labor-intensive, often results in highly non-robust systems, and usually results in design techniques that, have limited applicability. In contrast, with COINs we wish to solve the system design problems implicitly, via the 'adaptive' character of the RL algorithms of each of the agents. This COIN approach introduces an entirely new, profound design problem: Assuming the RL algorithms are able to achieve high rewards, what reward functions for the individual agents will, when pursued by those agents, result in high world utility? In other words, what reward functions will best ensure that we do not have phenomena like the tragedy of the commons, or Braess's paradox? Although still very young, the science of COINs has already resulted in successes in artificial domains, in particular in packet-routing, the leader-follower problem, and in variants of Arthur's "El Farol bar problem". It is expected that as it matures not only will COIN science expand greatly the range of tasks addressable by human engineers, but it will also provide much insight into already established scientific fields, such as economics, game theory, or population biology.
Predicting Trajectory Paths For Collision Avoidance Systems

This work was motivated by the idea of developing a more encompassing collision avoidance system that supported vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications. Current systems are mostly based on line of sight sensors that are used to prevent a collision, but these systems would prevent even more accidents if they could detect possible collisions before both vehicles were in line of sight. For this research we concentrated mostly on the aspect of improving the prediction of a vehicle’s future trajectory, particularly on non-straight paths. Having an accurate prediction of where the vehicle is heading is crucial for the system to reliably determine possible path intersections of more than one vehicle at the same time. We first evaluated the benefits of merging Global Positioning System (GPS) data with the Geographical Information System (GIS) data to correct improbable predicted positions. We then created a new algorithm called the Dead Reckoning with Dynamic Errors (DRWDE) sensor fusion, which can predict future positions at the rate of its fastest sensor, improving the handling of accumulated error while some of the sensors are offline for a given period of time. The last part of our research consisted in the evaluation of the use of smartphones’ built-in sensors to predict a vehicle’s trajectory as a possible intermediate solution for a V2V and V2I communications, until all vehicles have all the necessary sensors and communication infrastructure to fully populate this new system. For the first part of our research, the actual experimental results validated our proposed system, which reduced the position prediction errors during curves to around half of what it would be without the use of GIS data for prediction corrections. The next improvement we worked on was the ability to handle change in noise, depending on unavailable sensor measurements, permitting a flexibility to use any type of sensor and still have the system run at the fastest frequency available. Compared to a more common KF implementation that would run at the rate of its slowest sensor (1Hz in our setup), our experimental results showed that our DRWDE (running at 10Hz) yielded more accurate predictions (25-50% improvement) during abrupt changes in the heading of the vehicle. The last part of our research showed that, comparing to results obtained with the vehicle-mounted sensors, some smartphones yield similar prediction errors and can be used to predict a future position.
Actors and Aliens: Representations of Teenage Protagonists in Bestselling YA Fiction

This project explores the representation of teenagers in some of the most popular Young Adult Fiction (YA) books of the early 2010s. A dominant assumption within the scholarly discussion of YA is that YA books are books for young people that feature protagonists with whom young people personally identify. Through an analysis of nine American YA books, this thesis offers an overview of the sorts of protagonists often found in the novels on New Zealand’s YA bestseller lists. Reporting results from a questionnaire and one-on-one interviews conducted with young YA readers, it explores whether or not this model of being a teenager is one with which young people do actually identify. Using Erving Goffman’s 1959 theory of impression management as a lens, this thesis explores how the protagonists of the selected novels see their world as a stage upon which they are expected to act out socially acceptable roles. While female protagonists use strategies to meet this pressure and emerge as competent social actors, male protagonists find themselves governed by forces beyond their control that render them social aliens. Whereas female protagonists face negative consequences for being themselves, male protagonists are rewarded for doing so. Regardless of their acting ability, all protagonists are represented as confused, insecure and troubled. The selected texts thus support stereotypes about teenage girls, teenage boys and young people in general that differ from those of the past but are ultimately no less problematic. The participants in this project’s small mixed-methods study shared this view of the protagonists but did not generally identify with them. While they enjoyed reading these books, this was not necessarily because of the protagonists, for whom they felt more concern than empathy. Rather than reflecting any kind of ‘truth’ about what it means to be a teenager, it seems that the protagonists of the selected texts reflect how adult authors, editors and booksellers imagine teenagers to be. This is a significant finding given the rising numbers of adults who are reading YA and the declining numbers of teenagers who are reading for pleasure.
Nonlinear assignment problems : algorithms and applications

List of Figures. List of Tables. Preface. Contributing Authors. Introduction P. Pardalos, L. Pitsoulis. 1. Multi Index Assignment Problems: Complexity, Approximation, Applications F.C. R. Spieksma. 2. MD Assignment of Data Association A.B. Poore. 3. Target-Based Weapon Target Assignment Problems R.A. Murphey. 4. The Nonlinear Assignment Problem in Experimental High Energy Physics J.-F. Pusztaszeri. 5. Three Index Assignment Problem L. Qi, D. Sun. 6. Polyhedral Methods for the QAP V. Kaibel. 7. Semidefinite Programming Approaches to the Quadratic Assignment Problem H. Wolkowicz. 8. Heuristics for Nonlinear Assignment Problems S. Voss. 9. Symbolic Scheduling of Parameterized Task Graphs on Parallel Machines M. Cosnard, et al. 10. Decomposition Algorithms for Communication Minimization in Parallel Computing I.T. Christou, R.R. Meyer.
A Novel Integration Scheme for Audio Visual Speech Recognition

Automatic speech recognition (ASR) has been successfully applied to many real human computer interaction (HCI) applications; however, its performance tends to be significantly decreased under noisy environments. The invention of audio visual speech recognition (AVSR) using an acoustic signal and lip motion has recently attracted more attention due to its noise-robustness characteristic. In this paper, we describe our novel integration scheme for AVSR based on a late integration approach. Firstly, we introduce the robust reliability measurement for audio and visual modalities using model based information and signal based information. The model based sources measure the confusability of vocabulary while the signal is used to estimate the noise level. Secondly, the output probabilities of audio and visual speech recognizers are normalized respectively before applying the final integration step using normalized output space and estimated weights. We evaluate the performance of our proposed method via Korean isolated word recognition system. The experimental results demonstrate the effectiveness and feasibility of our proposed system compared to the conventional systems.