Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling upper body kinematics while using a transradial prosthesis

vi Chapter 1: Introduction 1 1.1 Anatomy and Approximations of the Human Upper Body 2 1.1.1 Torso 2 1.1.2 Shoulder Complex 2 1.1.3 Upper Arm 4 1.1.4 Forearm 5 1.1. 5 Hand 6 1.2 Other Models of the Human Upper Body 6 1.3 Range of Movement and Nomenclature 8 1.4 Transradial Prostheses 11 Chapter 2: The Model 13 2.1 Denavit and Hartenberg Parameters 13 2.2 Parameters of Model Links 14 2.3 Bilateral Configuration 15 Chapter 3: Analysis of the Model 16 3.1 Tasks & Trajectories 16 3.1.1 Opening a Door 17 3.1.2 Drinking From a Cup 17 3.1.3 Turning a Steering Wheel 17 3.1.4 Lifting a Box 18 3.2 Inverse Kinematics 18 3.2.1 Forward Kinematics 18 3.2.2 Jacobian & Pseudo Inverse 19 ii 3.2.3 Weighted Pseudo Inverse 19 3.3 Optimization Functions 20 3.3.1 Exploiting Redundancy 20 3.3.2 Joint Limits 21 Chapter 4: Model Programming 22 4.1 MATLAB File Library 22 4.1.1 ModelGUI.m 22 4.1.2 /trajectories 24 4.1.3 /configurations 24 4.1.4 Virtual Reality 24 Chapter 5: Verifying the Model 25 5.1 The Previous Model 25 5.2 Adapting to the New Model 27 5.3 Comparing Data 28 5.3.1 Testing Task Matching 28 Chapter 6: Results 30 6.1 Drinking From a Cup 30 6.2 Opening a Door 31 6.3 Turning a Steering Wheel 32 6.4 Lifting a Box 33 Chapter 7: Discussion 36
A Comparitive Study on Document Clustering Techniques

Information retrieval is the twenty first century’s biggest challenge as the information being generated daily accounts for billions of bytes. The retrieval process is the costliest process to tackle as it involves taking the right information from the right place and delivering for the needed ones within a specified time limit. Another major factor involving in this process is the time. Getting the correct information within a stipulated time is of a matter of concern. There lies the importance of clustering the web documents. Not only for information retrieval, but for a variety of processes can be done by using the clustering. Clustering is used to group a set of objects based on a specific set of criteria. A variety of clustering techniques is being used, but which among gives the optimal result with maximum performance matters. As the sizes of the documents as well as the number of documents are increasing day by day, performance really matters in the case of document clustering. This paper lights upon a comparative study on the various methods of document clustering using the background knowledge and without using the background knowledge. This paper aims at evaluating the document clustering by setting up tools to collect the web documents, pre-process the documents, cluster the documents based on a set of algorithms and evaluate each of the methods. The major clustering methods used for the comparison is the K-means clustering, Particle Swarm Optimization Clustering and other varieties. As said earlier the increasing number of documents contributes to a big data problem. This paper also investigates the significance of hadoop architecture in the document clustering with various parallel algorithms implemented on the same. This will give a clear idea on the techniques and trends in the document clustering and signifies the importance of each of the methods and finds the best one. Keywords— document clustering, k-means, particle swarm optimization, parallel
The tactical Intranet IPSec security concept

The IPSec protocols architecture that can be applied in tactical Intranet based on the IPv6 protocol stack for wireless environment is the subject of the paper. The potential usefulness of the new version of IP protocol is very important for tactical communication systems. Additionally, Internet Engineering Task Force (IETF) security working group proposes recommendations covering the RFC 2401, 2402, 2406, that describe the security architecture for Internet Protocol. These standards, published by IETF are discussed here in military requirements context. The NATO C3 Technical Architecture model also recommends these issues. The concept of the IPSec architecture in military systems is described in the paper. The position of the security applications designed for subscriber devices with reference to layered model is also presented. The concept presented here is defined for the tactical level.
Fundamentals Of Neural Networks

1. Introduction. 2. Simple Neural Nets for Pattern Classification. 3. Pattern Association. 4. Neural Networks Based on Competition. 5. Adaptive Resonance Theory. 6. Backpropagation Neural Net. 7. A Sampler of Other Neural Nets. Glossary. References. Index.
Finite‐time formation control of multiagent systems via dynamic output feedback

Finite‐time formation control of multiple second‐order agents via dynamic output feedback is investigated in this paper. Under the assumption that the velocities of all agents cannot be measured, a continuous consensus algorithm is first proposed such that the states of all agents will reach an agreement in finite time. Then, the consensus algorithm is applied to the finite‐time formation control, including stationary formation and moving formation, respectively. Rigorous proof shows that all agents will converge to the desired formation pattern in finite time. Finally, an example is given to verify the efficiency of the proposed method. Copyright © 2012 John Wiley & Sons, Ltd.
Effizientes Multimedia-Streaming für Mixed-Reality Applikationen

Moderne Virtual Reality-Systeme erlauben dem Anwendungsentwickler, Szenen aus einer Vielzahl unterschiedlicher Elemente und Komponenten aufzubauen. Nahezu alle Systeme unterstutzen statische Szenengraphen zur Beschreibung der raumlichen und logischen Organisation dieser Elemente. Daruber hinaus erlauben viele Systeme die Einbindung einfacher dynamischer Elemente, wie Filme oder Audiosequenzen, in die Szenenbeschreibung. Dieser Arbeit liegt die Anforderung zugrunde, ein bestehendes System zur Erstellung von Prototypen fur Virtual Reality-Anwendungen zu erweitern, um das Streaming audiovisueller Informationen zu unterstutzen. Zusatzlich konnen aber auch andere Informationen als Datenstrom ubertragen werden. Beispielsweise konnen Szenenbeschreibungen zur Laufzeit dynamisch in die bestehende virtuelle Umgebung eingefugt werden und damit eine grossere Flexibilitat der Szenenbeschreibung erreichen. Fur das Einbinden solcher Informationsstrome fehlte es jedoch an einem Basisformat, das neben der raumlichen und logischen Organisation auch die zeitliche Abfolge beschreibt und so den Zusammenhang zwischen Multimediadaten und der Szenenbeschreibung einer virtuellen Umgebung herstellt. Im Rahmen dieser Arbeit wurden bestehende Beschreibungsformate fur Multimediaprasentationen anhand der Anforderungen verglichen. Daruber hinaus wurde analysiert, welche Anforderungen in Zukunft zu erwarten sind, um ein moglichst flexibles System zu erhalten. Dafur wurde ein Bogen von der aktuellen multimedialen Konvergenz zum nachsten Entwicklungsschritt im Bereich Graphik und interaktive Systeme gespannt. Die Uberlegung ist, dass der Konversion der multimedialen Datenformate eine Konversion im Bereich der Darstellungs- und Kommunikationssysteme zur Folge hat. Diese zweite Konversion wird mit dem Begriff der Mixed Reality gleichgesetzt, welcher per Definition die Bereiche der Virtual Reality, des Teleconferencings, der Telepresence und der Augmented Reality umfasst. Daruber hinaus werden die moglichen Auswirkungen auf Ausdrucksformen und Prasentation von Informationen beleuchtet und an Beispielen verdeutlicht. Das Ergebnis der Untersuchung favorisiert MPEG-4 gegenuber anderen Beschreibungsformaten wie MHEG oder SMIL. Zusatzlich wurden auch die Anforderungen an Ubertragungsprotokolle im Hinblick auf multimediale Informationen betrachtet. Auch auf diesem Gebiet zeigte sich eine Uberlegenheit des MPEG-4 Systems durch seine Flexibilitat und Modularitat. Aufgrund des Umfangs des MPEG-4-Systems, blieb die Implementierung eines Prototypen rudimentar. Trotzdem konnte die Machbarkeit der Integration sowie die Schwachen bestehender MPEG-4 Implementierungen gezeigt werden, die deren Einsatz bislang unmoglich machen.
Visual Storytelling: Impact of Data visualization on citizens' health behaviors

Although data visualization is gaining in popularity due to its ease of use and learning, and superiority of presenting data in a pleasing manner, healthcare sector has lagged behind other industries in the use of data visualization. In order to understand the appropriateness of data visualization technique, especially storytelling, in improving public health, it is necessary to empirically investigate whether and how this technique could help general public understand complex healthcare datasets and gain insights. In this study, we present a preliminary conceptual framework and our proposed research design to conduct this study. Specifically, we employ the stimulus-organism-response (S-O-R) model as a framework, and Tableau storytelling features to undertake this investigation. By doing so, we will be able to further our understanding of data visualization with storytelling component, and of whether and how data visualization influence common citizens’ understanding about proper healthcare behaviors.
Analysis of E-Learning Implementation Cost Pools

Public German universities face risen expectations of students. Changes in the environment force them to increase service degrees while additional resources are not disposable. The usage of e-learning technologies is one possible lever to enhance service degrees while leaving the total budget nearly unchanged on the long run. To enable the organizational implementation of the new media, nevertheless, substantial investments are necessary up-front. In this work cost pools relevant for implementing e-learning technologies in an organization are identified based on a set of e-learning initiatives at the Johann Wolfgang Goethe-University in Frankfurt am Main. Using theoretical findings of language critique a considerable fraction of the total cost can be explained as set-up costs lowering variable costs in later project phases significantly.
VocBlast: A QUALITATIVE EVALUATION ON THE USE VOCABULARY MOBILE APP FROM TEACHERS' VIEWS

Technology brings everything under one roof especially through the development of mobile phones. Previously, everyone was excited about downloading new ringtone for their phones. It got even better through time when mobile phone users can use their preferred mp3 as a ringtone. Today, the iPhone and Android users are overwhelmed by the number of applications that can be downloaded to help them in daily lives. There are applications for health, productivity and photo editing. Recently, there has been renewed interest when educators integrate mobile learning (m-learning) in their teaching. VocBlast, for instance, is a newly-developed engineering vocabulary mobile application (app) that is developed to assist engineering students at tertiary level to improve their mastery of English language. The current study, therefore, reviews the content in VocBlast from the language teachers’ perspectives. E-mail interviews were conducted employing four (4) language teachers in Universiti Malaysia Pahang. Based on the result, the participants agreed that the use of VocBlast in teaching and learning process is very helpful and able to benefit both, teachers and students. The app provides an indirect learning experience to the latter. At the same time, the features that are available in the app are appropriate and useful. Although VocBlast is still in the progress of development, the result presented may facilitate improvements of the app itself. Other than that,VocBlast can be considered as a new alternative for students to diversify their vocabulary learning experience.
EMG prosthetic hand controller using real-time learning method

This paper reports the prosthetic hand controller discriminating ten forearm motions from two channels of EMG signals. The controller uses the real-time learning method that is defined as simultaneously controlling a prosthetic hand and learning to adapt to the operator's characteristics. In this method, the controller is divided into three units. The analysis unit extracts useful information for discriminating motions from EMG. The adaptation unit learns the relation between EMG and control command and adapts to the operator's characteristics. The trainer unit generates training data and makes the adaptation unit learn in real-time. In experiments, the proposed controller performs discriminating a maximum of ten forearm motions including four wrist motions and six hand motions. In an eight forearm motions experiment, the five subjects' average discriminating rate, which serves an index of accurate controlling, was 85.1%. Two groups occur from this result, one marks a high performance (91.7%) and another does not (75.2%). The paper discusses the factors of this difference in performance from both phases of training and reasons that the low proficiency leads to undesirable results in the latter groups. Besides, in the ten forearm motions experiment the average discriminating rate of three subjects who achieve high performance in the previous experiment was 91.5%. This paper concludes that the effectiveness of the real-time learning method is confirmed by these experiments.
Integrating Web Content Mining into Web Usage Mining for Finding Patterns and Predicting Users’ Behaviors

With the increased confidence in the use of the Internet and the World Wide Web, the number of electronic commerce (e-commerce) transactions is growing rapidly. Therefore, finding useful patterns and rules of users’ behaviors has become the critical issue for e-commerce and can be used to tailor e-commerce services in order to successfully meet the customers’ needs. This paper proposes an approach to integrate Web content mining into Web usage mining. The textual content of web pages is captured through extraction of frequent word sequences, which are combined with Web server log files to discover useful information and association rules about users’ behaviors. The results of this approach can be used to facilitate better recommendation, Web personalization, Web construction, Website organization, and Web user profiling.
Acoustics and Psychoacoustics

From the Publisher: Acoustics and Psychoacoustics is a comprehensive book designed to provide a broad-ranging introduction to the subject of acoustics including the principles of human perception of sound, sometimes called psychoacoustics. It is ideal for students of music technology, sound recording, traditional music and acoustics, as well as engineers studying audio, multi-media and communications systems, and is geared very much towards those who want a practical understanding of how real musical sounds behave and are perceived in real spaces. It will also help those who need to use acoustics principles in the design of equipment, instruments and rooms and it will benefit those who make recordings. Whilst introducing all of the main fundamentals the text is easy to read and avoids non-essential mathematics. It is written for engineers, musicians and students, and there are plenty of worked examples for study.
Optical wireless: the story so far

We present a broad review of the optical wireless field, from early experiments through to today's high-performance systems. Emphasis is placed on understanding the benefits and limitations of optical wireless, all of which ultimately define the applications. A variety of systems are examined, each incorporating a different technological solution to suit the particular application.
Iterative feedback tuning and closed-loop output error identification applied to Smith predictors

In this paper, we study the applicability of two algorithms to Smith predictor dead time compensation. The first algorithm is Iterative Feedback Tuning (IFT), a model free gradient descent controller tuning tool. Refer to [1, 2] for further details. The second algorithm is Closed-Loop Output Error (CLOE) identification. This identification algorithm minimizes the difference between the achieved closed-loop response and the response of a closed-loop output error predictor, i.e. it is an identification algorithm that is tailored to closed-loop problems. Refer to [4] for further details.
A class AB CMOS current mirror with low-voltage capability

This paper presents a novel performance class AB current mirror. The circuit adopts an active-bootstrapped technique which allows low-voltage operations to be achieved. Moreover, the proposed solution exhibits high accuracy, linearity and an extremely high output resistance, without compromising frequency response. The excellent performance of the circuit is confirmed by simulations on a design example implemented in a standard 0.8 /spl mu/m CMOS technology.
Knowledge Discovery in Databases

From the Publisher: Knowledge Discovery in Databases brings together current research on the exciting problem of discovering useful and interesting knowledge in databases. It spans many different approaches to discovery, including inductive learning, bayesian statistics, semantic query optimization, knowledge acquisition for expert systems, information theory, and fuzzy 1 sets. The rapid growth in the number and size of databases creates a need for tools and techniques for intelligent data understanding. Relationships and patterns in data may enable a manufacturer to discover the cause of a persistent disk failure or the reason for consumer complaints. But today's databases hide their secrets beneath a cover of overwhelming detail. The task of uncovering these secrets is called "discovery in databases. " This loosely defined subfield of machine learning is concerned with discovery from large amounts of possible uncertain data. Its techniques range from statistics to the use of domain knowledge to control search. Following an overview of knowledge discovery in databases, thirty technical chapters are grouped in seven parts which cover discovery of quantitative laws, discovery of qualitative laws, using knowledge in discovery, data summarization, domain specific discovery methods, integrated and multi-paradigm systems, and methodology and application issues. An important thread running through the collection is reliance on domain knowledge, starting with general methods and progressing to specialized methods where domain knowledge is built in. Gregory Piatetski-Shapiro is Senior Member of Technical Staff and Principal Investigator of the Knowledge Discovery Project at GTELaboratories. William Frawley is Principal Member of Technical Staff at GTE and Principal Investigator of the Learning in Expert Domains Project.
Digital Audio Editing

Follow up what we will offer in this article about digital audio editing. You know really that this book is coming as the best seller book today. So, when you are really a good reader or you're fans of the author, it does will be funny if you don't have this book. It means that you have to get this book. For you who are starting to learn about something new and feel curious about this book, it's easy then. Just get this book and feel how this book will give you more exciting lessons.
The Simulation of Logical Networks (SLN)

SLN (Simulation of Logical Networks) is a new simulation language designed specifically to provide concepts and facilities needed to model and simulate logical networks. Logical networks provide a convenient and natural means of expressing decision processes when a general-purpose discrete-event simulation facil ity is unnecessary. SLN accepts a node and branch description of a logical network and automatically handles the construction and execution of the simulation. FORTRAN can be mixed with the SLN statements with few restrictions. SLN is highly efficient, simulating only logical networks and working as a preprocessor generating FORTRAN source which can be compiled, linked, and executed. The SLN library includes facilities for generating random variates, collecting and reporting statistics, monitoring simulation execution, and tailoring the simulation output. SLN is portable and designed for use in an environment lacking a modeling tradition.
Large-scale dynamic optimization using teams of reinforcement learning agents

Recent algorithmic and theoretical advances in reinforcement learning (RL) are attracting widespread interest. RL algorithms have appeared that approximate dynamic programming (DP) on an incremental basis. Unlike traditional DP algorithms, these algorithms do not require knowledge of the state transition probabilities or reward structure of a system. This allows them to be trained using real or simulated experiences, focusing their computations on the areas of state space that are actually visited during control, making them computationally tractable on very large problems. RL algorithms can be used as components of multi-agent algorithms. If each member of a team of agents employs one of these algorithms, a new collective learning algorithm emerges for the team as a whole. In this dissertation we demonstrate that such collective RL algorithms can be powerful heuristic methods for addressing large-scale control problems. Elevator group control serves as our primary testbed. The elevator domain poses a combination of challenges not seen in most RL research to date. Elevator systems operate in continuous state spaces and in continuous time as discrete event dynamic systems. Their states are not fully observable and they are non-stationary due to changing passenger arrival rates. As a way of streamlining the search through policy space, we use a team of RL agents, each of which is responsible for controlling one elevator car. The team receives a global reinforcement signal which appears noisy to each agent due to the effects of the actions of the other agents, the random nature of the arrivals and the incomplete observation of the state. In spite of these complications, we show results that in simulation surpass the best of the heuristic elevator control algorithms of which we are aware. These results demonstrate the power of RL on a very large scale stochastic dynamic optimization problem of practical utility.
Assessing the performance of multiobjective genetic algorithms for optimization of a batch process scheduling problem

Scheduling optimization problems provide much potential for innovative solutions by genetic algorithms. The complexities, constraints and practicalities of the scheduling process motivate the development of genetic algorithm (GA) techniques to allow innovative and flexible scheduling solutions. Multiobjective genetic algorithms (MOGAs) extend the standard evolutionary-based genetic algorithm optimization technique to allow individual treatment of several objectives simultaneously. This allows the user to attempt to optimize several conflicting objectives, and to explore the trade-offs, conflicts and constraints inherent in this process. The area of MOGA performance assessment and comparison is a relatively new field, as much research concentrates on applications rather than the theory. However, the theoretical exploration of MOGA performance can have tangible effects on the development of highly practical applications, such as the process plant scheduling system under development in this work. By assessing and comparing the strengths, variations and limitations of the developing MOGA using a quantitative method, a highly efficient MOGA can develop to suit the application. The user can also gain insight into behaviour the application itself. In this work, four MOGAs are implemented to solve a process scheduling optimization problem; using two and five objectives, and two schedule building rules.