Miracle-dz/UniFAR-FTUs huggingface dataset:
Static Characteristic Analysis of a New Type of Pneumatic Bending Joint

Aiming at the bending angle limit of existing pneumatic bending joints at low pressure,an idea of using structural angle to implement the bending action is proposed.And a new type of pneumatic bending joints with two flexible pneumatic actuators(FPAs) as its drive source is designed.This joint includes one T-type structure,two rotating structures which can perform circular motion around the hinge,and two symmetrical FPAs.And two symmetrical FPAs are fixed on the T-type structure and the rotating structure.The two FPAs stretch to push rotating structure to move when inner air pressures are changed,which leads to an angle between rotating structures,and thus bending motion of the joints is achieved.The static characteristics of the turning end of FPA on the bending joint are analyzed based on the moment equilibrium principle.Simulation analysis and experimental results show that angle-pressure ratio of the new pneumatic bending joint is higher than traditional ones.It means that the larger bending angle can be gotten at lower pressure.Besides,the bending angle of the bending joint is nonlinear with the air pressure inside the FPA and the bending angle changes with the structure parameters.
Blood Vessel Detection from Fundus Image for Diabetic Retinopathy Patients using SVM

Diabetic retinopathy, the most common diabetic eye disease, occurs when blood vessels in the retina change, which lead to blindness if it is not treated. Blood vessel detection from Retinal images is a vital and challenging task for even experienced ophthalmologists. In this project, we have proposed a computer approach for the detection of diabetic retinopathy stage using color fundus images. The features are extracted from the fundus images using the image processing techniques namely edge enhancement with Kirsch template, grayscale conversion, histogram equalization, morphological processing, object classification and then fed to the support vector machine (SVM) which automatically detect the blood vessels as normal or Abnormal. This study attains the sensitivity of 96.07%, which is superior to the various other well-known techniques. Keywords—Fundus image, Diabetic Retinopathy, Kirsch template, sensitivity, Support vector machine (SVM)
Semiotics and levels of representation in complex information processing systems

It has been claimed that representational models of cognition are not adequate for explaining the behavior of complex informational processing systems. The reason given is that these systems are not necessarily symbolic and do not obey the rules of a grammar. Can there exist systems that, although representational are not of the symbolic kind? Semiotics has proposed interesting concepts that can be pertinent for modeling such models.
A survey on summarization and sentiment analysis of social content

- The use of short-text messages such as tweets, Facebook comments, etc. are being created and shared at a very high rate. These short messages, are very informative, but at the same time they are very overwhelming.The messages contain large amount of noise and redundancy so reading such messages becomes very difficult for users. Especially for those users who visit their social accounts after long time or after few hours. Due to the widespread use of social medium different topics are discussed and comments are being generated by users. The user needs to read many messages even to get idea of what is being discussed or going on at that particular movement. Many times it is observed that after reading n number of messages user regrets as the topic was not of his/her interest. The proposed system will help user to solve this major problem by firstly creating a summary of the messages according to user preferences and then provide user with advance features such as predicting the sentiment of the summary and also the heat of that current topic. Thus user will get an opinion about the current topic and he can decide whether the topic is or not of his/her interest.
Utilizing surplus timing for power reduction

Multiple Vdd's, multiple Vth's, and multiple transistor width for utilizing surplus timing in non-critical paths for power reduction is investigated. Theoretical models are developed from which rules of thumb for optimum Vdd's, Vth's, and W's are derived, as well as knowledge for future design.
Proceedings of the 14th international conference on Compilers, architectures and synthesis for embedded systems

Welcome to the 14th edition of Compilers, Architectures and Synthesis for Embedded Systems -- CASES'11! We are very pleased with this year's program which includes a special session on biochips, several invited talks and an exciting keynote by Arvind who is the Johnson Professor of Computer Science and Engineering at MIT. We are also following a new format where each twohour session has three papers and a final half hour poster session for more close one-on-one interaction with the presenters in the session. Please take advantage of this format to leave no question unanswered! CASES is proud to be a founding member of one of the three central conferences of Embedded Systems Week (ESWEEK) which was first organized in 2006. This year, ESWEEK is being held in Taipei, home of a wide proliferation of embedded systems and technology companies. Out of 61 submissions, the CASES program committee accepted 23. We organized the papers into a single track of nine sessions spread over three days. The program committee spent extra time this year in two full-day meetings -- one virtual and one face to face -- to finalize the program. All in all, members of the program committee spent approximately 16 hours meeting and discussing the papers to make sure all decisions were based on actual technical considerations and not just the raw scores from reviewers. As we reviewed the submissions, program committee discussions and the emergent program, a few trends point to exciting possibilities for meaningful advances in the state of the art in embedded systems architecture and software. Foremost among these is the convergence of the erstwhile communities of electronic design automation and computer architecture. The algorithms, methods and tools community which were already in sync in the compiler domain are finding new common ground and new ways of looking at computer architectures from chip level to complete systems. Indeed, Arvind's keynote talk for CASES represents such a shift: a quintessential computer architect who is devising new methods for capture, modeling and synthesis of interfaces between hardware and software. Another trend is made possible by substantial technical gains in parallelization and virtualization methods in embedded systems. Just as microprocessors strictly surpassed mainframes in terms novel architectural features in the mid-1990s, the embedded systems community is at the threshold of surpassing sophistication, value and utilization of methods for virtualization and security. Our session on this subject along with an invited talk by Gernot Heiser of NICTA reflect this trend. We have assembled a program that reflects these trends and goes far beyond to describe the latest in thinking, demonstration and practice in compilers, architecture and software for embedded systems. No doubt you will enjoy these discussions as much as we had fun in putting such a program together.
Discriminative feature-rich models for syntax-based machine translation.

This report describes the campus executive LDRD %E2%80%9CDiscriminative Feature-Rich Models for Syntax-Based Machine Translation,%E2%80%9D which was an effort to foster a better relationship between Sandia and Carnegie Mellon University (CMU). The primary purpose of the LDRD was to fund the research of a promising graduate student at CMU; in this case, Kevin Gimpel was selected from the pool of candidates. This report gives a brief overview of Kevin Gimpel's research.
THE EFFECT OF DATA PREPROCESSING ON THE PERFORMANCE OF ARTIFICIAL NEURAL NETWORKS TECHNIQUES FOR CLASSIFICATION PROBLEMS

The artificial neural network (ANN) has recently been applied in many areas, such as medical, biology, financial, economy, engineering and so on. It is known as an excellent classifier of nonlinear input and output numerical data. Improving training efficiency of ANN based algorithm is an active area of research and numerous papers have been reviewed in the literature. The performance of Multi-layer Perceptron (MLP) trained with back-propagation artificial neural network (BP-ANN) method is highly influenced by the size of the data-sets and the data-preprocessing techniques used. This work analyzes the advantages of using pre-processing datasets using different techniques in order to improve the ANN convergence. Specifically Min-Max, Z-Score and Decimal Scaling Normalization preprocessing techniques were evaluated. The simulation results showed that the computational efficiency of ANN training process is highly enhanced when coupled with different preprocessing techniques.
Shedding new light on solitary waves observed in space

Electrostatic solitary waves are routinely observed by the Cluster Wideband (WBD) plasma wave receiver as the Cluster spacecraft cross boundary layers and regions of turbulence. These solitary waves are observed in the electric field waveform data as isolated pulses of various shapes, but primarily in the bipolar and tripolar forms. The amplitudes of the solitary waves appear to follow a somewhat general trend of increasing amplitude with increasing background magnetic field strength. Thus, the
Mine Multi-sensor Maximum Likelihood Estimation Data Fusion Algorithm ⋆

After an overall consideration of the differences in the measurement accuracy of various sensors, an improved algorithm based on consensus data fusion has been proposed, the measurement accuracy of which is reflected through weight coefficient. It is indicated in simulation that this approach can effectively reduce data variation extent when there is disturbance in sensors, improving fusion accuracy and anti-interference capability. This algorithm will be applied in the data fusion of temperature sensor, gas concentration sensor in intelligent monitoring system of coal mine.
A New Approach To Solve Aircraft Recovery Problem

When disruptions occur, the airlines have to recover from the disrupted schedule. The recovery usually consists of aircraft recovery, crew recovery and passengers’ recovery. This paper focuses on aircraft recovery. Take the total cost of assignment, cancelation and delay as an objective; we present a more practical model, in which the maintenance and other regulations are considered. Then, we present a so-called iterative tree growing with node combination method. By aggregating nodes, the possibility of routings is greatly simplified. So, it can give out the solution in more reasonable time. Finally, we use data from a main Chinese airline to test the solution algorithm. The experimental results state that this method could be used in aircraft recovery problem. Keywords-aircraft recovery; airlines optimal recovery ; airlines recovery; recovery algorithm
Survey of active learning for networked data

Classification in networked data is a popular research of complex network. Because of the large scale of networked data and the shortage of training data, active learning, which is an effective classification method for sparse data in machine learning, is often applied to networked data classification problems. Introduced in this paper are classification methods based on active learning for networked data classification problems with basic concepts and algorithms. Finally, according to the existing research, some problems for the future developing and research of networked data classification issues is presented.
A probabilistic approach to sound classification

Disclaimer This document contains a student thesis (bachelor's or master's), as authored by a student at Eindhoven University of Technology. Student theses are made available in the TU/e repository upon obtaining the required degree. The grade received is not published on the document as presented in the repository. The required complexity or quality of research of student theses may vary by program, and the required minimum study period may vary in duration.
ADAPTIVE REAL-TIME TOOL FOR HUMAN GAIT EVENT DETECTION USING A WEARABLE GYROSCOPE

The development of robust algorithms for human gait analysis are essential to evaluate the gait performance, and in many cases, crucial for diagnosing gait pathologies. This work proposes a new adaptive tool for human gait event detection in real-time, based on the angular velocity recorded from one gyroscope placed on the instep of the foot and in a finite state machine with adaptive decision rules. The signal was segmented to detect 6 events: Heel Strike (HS), Foot Flat (FF), Middle Mid-Stance (MMST), Heel-Off (HO), Toe-Off (TO), and Middle Mid-Swing (MMSW). The tool was validated with healthy subjects in ground-level walking using a treadmill, for different speeds (1.5 to 4.5 km/h) and slopes (0 to 10%). The results show that the tool is highly accurate and versatile for the detection of all events, as indicated by the values of accuracy, average delays and advances (HS: 99.96%, -7.95 ms, and 9.85 ms; FF: 99.48%, -4.95 ms, and 9.35 ms; MMST: 98.26%, -36.54 ms, and 16.38 ms; HO: 98.87%, -22.71 ms, and 18.62 ms; TO: 95.95%, -6.80 ms, 14.38 ms; MMSW: 96.06%, -3.45 ms; 0.15 ms, respectively). These findings suggest that the proposed tool is suitable for the real-time gait analysis in real-life activities.
Same Same, but Different: Word and Sentence Reading in German and English

ABSTRACT The current study compared eye fixation patterns during word and sentence processing in a consistent and an inconsistent alphabetic orthography. German and English children as well as adults matched on word reading ability read matched sentences while their eye fixation behavior was recorded. Results indicated that German children read in a more small-unit plodder-like style with more diligent first-pass reading and less rereading. In contrast, English children read in a more large-unit explorer-like style with a greater tendency to skip words, and more regressions. It is important that these cross-linguistic processing differences largely persisted in the adult readers. Orthographic consistency thus influences both local word recognition and global sentence processing in developing and skilled readers.
A Comparative evaluation of three automatic language identification approaches for Indian Languages

Abstract— In the present study, three language identification (LID) approaches are compared for a set of three Indian languages. However, the number of languages can be increased. One of the approaches is the Hidden Markov Model (HMM) based Phonetic Engine (PE), another is the Gaussian Mixture Model based Universal Background Model (GMM-UBM) classifier and the remaining one is a prosodic feature based classification system. The PE belongs to the category of Explicit LID techniques while the GMM-UBM system and the prosodic feature based classifier fall into the group of Implicit LID techniques. Basically, Explicit LID techniques require a segmented and phonetically labelled speech corpus, while the Implicit LID techniques do not require any phonetic description of the data. All the systems are used here in identifying a set of test data from three Indian languages, Manipuri, Assamese and Bengali. The selection of these languages is made on the basis of the wide range of usages of them in North Eastern India and at the same time no identification tasks were carried out so far for a database inclusive of these three languages together. The purpose of this comparison is to check the LID efficiency among the three approaches. In the experiments, it is found that the prosodic feature based approach can only be used for a broader classification of languages into two categories such as tonal and non tonal. Such a system can’t be used alone to completely identify a language. However, the other two approaches can suitably be used for automatic identification of the languages. The identification rate (IDR) of the PE is superior to the GMMUBM classifier. The average IDR reported with PE is 99% while for the GMM-UBM classifier it is found to be 96.94% with the same speech corpus being in use. However, the data preparation task is a little more expensive in PE than that of GMM-UBM classifier. Thus, the compensation for accuracy may be paid with the cost incurred in data preparation.
Routing Algorithm based on Mobility Prediction

This thesis first makes a deep research on routing protocols in real-life scenarios where the location information of mobile nodes is available. Considering the practical mobility characteristics, a new interpolation prediction algorithm of mobile location is proposed to solve the current problem of speed prediction, and also expands the scope of mobility prediction algorithm. After that, a routing protocol based on the mobility prediction is proposed. In spray phase, multiple copies of the message are distributed in a binary tree way, which can improve the transmission performance and effectively control communication overhead. In forwarding phase, when a node carrying a message copy encounters another node, it will carry out mobile location prediction, and forward the message to the node which will be closer to the destination in the future. This routing protocol reduces the complexity of mobile information collection, and solves the classic routing protocol’s problem in delivering messages caused by just waiting to encounter the destination. The proposed routing protocol achieves a better performance in the message delivery probability and delivery delay.
Model and Analysis of Cloud Storage Service Reliability Based on Stochastic Petri Nets

HDFS is a distributed file system of Hadoop cluster, providing storage service for cloud computing. Files in HDFS are broken into block-sized chunks, each block is replicated to physically separated machines. Some studies have been conducted to HDFS, but no work focuses on the performance analysis of HDFS. In this paper, a concise mathematic tool is adopted, Stochastic Petri Nets (SPNs), to establish a system model of HDFS. By the general SPNs model, the reliability, availability and maintainability of HDFS can be measured, and a hierarchical decomposition method is proposed to avoid the state space explosion problem as well, which give a theoretical basis for further studying on the optimal reliability of cloud services.
Segmentation of Cells and Sub-cellular Structures from Microscopic Recordings

In life-sciences, technical innovations in microscopy and the ever growing availability of modern fluorescence microscopes allow for the investigation of more and more complex biological processes. To facilitate the analysis of the recorded microscopic data, the development of automated methods is indispensable, because these methods allow for the evaluation of a sufficient amount of data without bias. The analysis of fluorescence microscopic data comes with some specific challenges. The recorded fluorescence signal is noisy, the signal intensity of the identical structure potentially varies from one recording point to the other and the recording resolution is strongly anisotropic due to technical limitations. Additionally, the optical system introduces blur, which is commonly described by a convolution with the so called point spread function. The presented thesis primarily focuses on the segmentation of objects, mostly cells, cell nuclei, or other cell organelles, from microscopic recordings. Another key aspect is the deconvolution of the recordings in order to alleviate the blur introduced by the optical system. Especially when processing data from widefield microscopy, deconvolution is often a crucial preprocessing step. The presented methods for blind deconvolution are based on the regularization of the reconstructed point spread function. On our test datasets, they lead to improved, sharpened images with a better signal to noise ratio. A comparison to state-of-the-art methods confirms the aptitude of our approach. The proposed methods for the segmentation of objects from volumetric data are based on parametric active surfaces. An important contribution is the generation of appropriate data terms for the representation of the object boundaries in noisy data with weak fluorescence signal. Thus, we present methods for the generation of data terms that allow for the segmentation of fluorescently labeled cell nuclei and nucleoli from widefield microscopic recordings. Furthermore, we propose a method to automatically generate new, very specific data terms for the segmentation of objects from volumetric data based on very simple user input. Experiments show that our methods allow an accurate segmentation of the objects under investigation, which, with conventional active surface and level set methods, could not be achieved. Thereby, our dataterms can be easily adapted to changing recording conditions. Finally, we present a method that allows to learn topological knowledge that can be used for the simultaneous classification and segmentation of objects. This approach is based on hierarchical Markov Random Fields. By means of this method, the segmentation accuracy in a dataset of electron microscopic recordings could be greatly improved compared to conventional Markov Random Fields.
An Academic Search Engine for Personalized Rankings

Rapidly increasing information on the Internet and the World Wide Web can lead to information overload. Search engines become important tools to help WWW users to discover information. Exponential increases in published research papers, academic search engines become indispensable tools to search for papers in their expertise and related fields. In order to improve the quality of search, an academic search engines’ capability should be enhanced. This paper proposes a search engine for personalized rankings. In order to evaluate the performance of personalized rankings, thirty-five graduate students from the Department of Web Engineering and Mobile Application Development at Dhurakij Pundit University are participants in the research experiment. Participants are asked to use a prototype of an academic search engine to find and bookmark any research papers according to their interests, which would guarantee that each participants’ list of interesting research papers could be recorded. Normalized Discounted Cumulative Gain (NDCG) is used as a metric to determine the performance of the personalized rankings. The experiments suggest that the personalized rankings outperform the original search rankings. Hence, the proposed academic search engine with personalized ranking benefits research paper discovery.
Real object pickup method for real and virtual modes of integral imaging

Abstract. Pickup method adopting the modified algorithm to generate the elemental image from virtual objects is proposed to obtain the elemental image for real objects. In the proposed method, the number of capturing processes is reduced compared with the conventional multiple capturing method. The pseudoscopic image problem can be resolved by controlling the position and the direction of the imaging device in the proposed pickup system. The telecentric lens system is used to capture the orthographic scenes, which are divided and compounded into the elemental image. The validity of the proposal is proved by the experimental results of the pickup and the reconstruction.