Miracle-dz/UniFAR-FTUs huggingface dataset:
Measurement of steady state functional connectivity in the human brain using functional magnetic resonance imaging

Functional magnetic resonance imaging (fMRI) is a noninvasive imaging technique capable of mapping cognitive networks in the human brain that has become widely used for both research and clinical applications. The activity of focal regions within the brain can be inferred based on their blood oxygen level dependent (BOLD) signal changes through time by recording a series of images while subjects perform carefully designed tasks. Steady state functional connectivity methods generate similar maps of neural networks through analyses of underlying activity. Resting state functional connectivity measurements reduce the demands placed on patient/subject compliance and can potentially extend fMRI to a wider range of applications. However, the factors that affect measurements of steady state functional connectivity remain unclear, and methods of making these measurements may be further improved. This dissertation addresses three different aspects of functional connectivity measured with fMRI. First, we study the effects of cognitive load on measurements of functional connectivity in the working memory and default mode networks, building on previous work in the motor network. We report increases in functional connectivity within both networks, and present evidence of changing connectivity between them. Second, we apply functional connectivity analyses to electroencephalography and fMRI data recorded simultaneously to investigate the regions underlying variance in frontal theta power. Our results show both positive and negative correlations to theta power across working memory loads, and changing correlations in the parahippocampal gyrus, among other regions. Third, we demonstrate two methodological improvements to the measurement of functional connectivity using data acquired at ultra high field (7T) and applying nonlinear measurements of connectivity. We show that detection and significance of functional connectivity can be improved by decreasing voxel volumes to sizes that are not practically achievable without ultra-high magnetic fields, presumably due to decreases in partial volume effects. In addition, we show that mutual information can be used to detect functional connectivity between regions that are commonly left unidentified by measurements based on linear correlation coefficients.
A formal approach to modeling and performance analysis of shared channels for real-time services in W-CDMA 3G systems

The third generation of mobile systems will be able to support a plethora of novel and challenging services in an efficient way; multimedia and data applications produce intermittent, variable bit-rate digital flows that can profit of a packet-switched resource allocation technique, whose performance in terms of system capacity, when supporting real-time VBR traffic and adopting a simple call admission control scheme, is illustrated. This non-trivial case study, further complicated by the dependence of W-CDMA capacity on the interference level and QoS requirements, is investigated with the help of a formal approach. Instead of using a simulation environment, a tool for performance evaluation is exploited, where a formal specification of the system is built, verified for proper behavior and analyzed to obtained the steady state probability of the Markov chain equivalent to the specification. The rigorousness of the formal notation and the verifiability of the specification enable one to speed up the modeling phase and give more confidence in the numerical results obtained.
Optical displacement sensor

Object of the present invention is to provide an object to be measured can correctly detect the optical displacement sensor, even if the position change of the target to be measured greater than its thickness. Such an optical displacement sensor comprising: light projecting means for projecting the light to the target to be measured; a light receiving means for receiving the projected light reflected by the measured object; displacement amount calculating means for receiving optical output means to calculate the amount of displacement of the target to be measured; the difference generating means, to the amount of displacement for sampling at fixed time intervals, and determines the difference between the displacement magnitude; means comparing the difference, which was the difference in the positive and negative threshold values ​​a threshold value; and a target detection processing means for detecting a signal representing the presence or absence of the object to be measured based on the comparison output. The object detection signal processing means on detection result of the comparison of the difference value with one of a positive threshold and a negative threshold and a difference of the comparison result, and to turn off the detection signal according to the detection result to another.
Control Algorithms for Rearrangeable Clos Networks

Recently interest in rearrangeable Clos networks has risen again, due to their potential uses in data networks and computing systems. Much research has been directed towards the construction of efficient control algorithms. However, the literature has seemed not to be aware of an important link between the control algorithms and the edge coloring algorithms for bipartite graphs. In this paper we show that a recent edge coloring algorithm of Gabow can be used to obtain a control algorithm faster than any existing one.
Multicore Processors - A Necessity

As personal computers have become more prevalent and more applications have been designed for them, the end-user has seen the need for a faster, more capable system to keep up. Speedup has been achieved by increasing clock speeds and, more recently, adding multiple processing cores to the same chip. Although chip speed has increased exponentially over the years, that time is ending and manufacturers have shifted toward multicore processing. However, by increasing the number of cores on a single chip challenges arise with memory and cache coherence as well as communication between the cores. Coherence protocols and interconnection networks have resolved some issues, but until programmers learn to write parallel applications, the full benefit and efficiency of multicore processors will not be attained. Background The trend of increasing a processor‟s speed to get a boost in performance is a way of the past. Multicore processors are the new direction manufacturers are focusing on. Using multiple cores on a single chip is advantageous in raw processing power, but nothing comes for free. With additional cores, power consumption and heat dissipation become a concern and must be simulated before layout to determine the best floorplan which distributes heat across the chip, while being careful not to form any hot spots. Distributed and shared caches on the chip must adhere to coherence protocols to make sure that when a core reads from memory it is reading the current piece of data and not a value that has been updated by a different core. With multicore processors come issues that were previously unforeseen. How will multiple cores communicate? Should all cores be homogenous, or are highly specialized cores more efficient? And most importantly, will programmers be able to write multithreaded code that can run across multiple cores?
2002 Special Issue Statistical tools to assess the reliability of self-organizing maps

Results of neural network learning are always subject to some variability, due to the sensitivity to initial conditions, to convergence to local minima, and, sometimes more dramatically, to sampling variability. This paper presents a set of tools designed to assess the reliability of the results of self-organizing maps (SOM), i.e. to test on a statistical basis the confidence we can have on the result of a specific SOM. The tools concern the quantization error in a SOM, and the neighborhood relations (both at the level of a specific pair of observations and globally on the map). As a by-product, these measures also allow to assess the adequacy of the number of units chosen in a map. The tools may also be used to measure objectively how the SOM are less sensitive to non-linear optimization problems (local minima, convergence, etc.) than other neural network models. q 2002 Elsevier Science Ltd. All rights reserved.
Perceptual Learning Under a Cochlear Implant Simulation

Adaptation to the acoustic world following cochlear implantation does not typically include formal training or extensive audiological rehabilitation. Can cochlear implant (CI) users benefit from formal training, and if so, what type of training is best? This study used a pre/post-test design to evaluate the efficacy and generalization of training in normal hearing subjects listening to CI simulations (8-channel sinewave vocoder). Subjects were trained with words (simple or complex), sentences (meaningful or anomalous), or environmental stimuli, and then were tested using an open-set identification task. Subjects were trained on only one type of material but were tested on all materials. All groups showed significant improvement as a result of training, which successfully generalized to some, but not all stimulus materials. For easier tasks, all types of training generalized equally well. For more difficult tasks, training specificity was observed. Training on speech did not generalize to the recognition of environmental signals; however, training on environmental signals successfully generalized to speech. These data demonstrate that the perceptual learning of degraded speech is highly context-dependent and that the specific stimulus materials that a subject experiences during training have a substantial impact on generalization to new materials.
On external calibration of analog-to-digital converters

External calibration and compensation of analog-to-digital converters is considered. Two novel methods are presented. Both methods employ multiple calibration frequencies in order to improve the wide-band performance of the converter. Also, a dynamic table indexing is introduced to further improve the performance. A recursive sine-wave reconstruction filter algorithm is developed for calibration purposes. The proposed methods are evaluated using experimental converter data. Results indicate that the dynamic indexing provides good correction performance, also at frequencies not used during calibration. Thus, wide-band calibration can be achieved.
Improving Aircraft Collision Risk Estimation using the Cross-Entropy Method

Estimates of mid-air collision risk are obtained through Monte Carlo simulation of encounters sampled from a probabilistic airspace model. Due to the rarity of collision events, typically millions of simulations are required. A technique known as importance sampling has been used in the past to bias the sampling on trajectories that are likely to result in collisions. However, determining a suitable importance sampling distribution is difficult. This paper explores the use of the cross-entropy method to adaptively determine a suitable sampling distribution and analyzes its performance. The results show that reliable estimates of collision risk can be obtained with only a fraction of the computational cost required by methods used in past studies.
Survey of Data Mining Techniques used in Healthcare Domain

Health care industry produces enormous quantity of data that clutches complex information relating to patients and their medical conditions. Data mining is gaining popularity in different research arenas due to its infinite applications and methodologies to mine the information in correct manner. Data mining techniques have the capabilities to discover hidden patterns or relationships among the objects in the medical data. In last decade, there has been increase in usage of data mining techniques on medical data for determining useful trends or patterns that are used in analysis and decision making. Data mining has an infinite potential to utilize healthcare data more efficiently and effectually to predict different kind of disease. This paper features various Data Mining techniques such as classification, clustering, association and also highlights related work to analyse and predict human disease.
Dual-wavelength Packet Signal Storage with SOA-based Dual Loop Optical Buffer

In this paper, we investigate the dual-wavelength packet signal storage in the semiconductor optical amplifier (SOA)-based dual-loop optical buffer (DLOB). The loadable and unloadable function of the DLOB is implemented by the cross phase modulation induced by the SOA. After a temporal model is proposed for the dual-wavelength packet signal interaction, the influence of nonlinear phase shift on the chirp and distortion of the stored packet pulse is examined for the case of the dual wavelength loadable operation. It is found that, when the overlapping area of the stored dual-wavelength pulse is less than half of the pulsewidth, the distortion of output pulses after storage is negligibly small. However the most severe distortion appears when the dual-wavelength pulses overlap completely. The peak power of output packet pulse is approximately reduced by 30%. The observed experimental results agree well with the theoretical calculation. In a conclusion, these works confirm the validity of the proposed model to investigate the dual-wavelength packet signal operation.
A step up with the HARVARD system: the HARVARD-g system

The HARVARD system is a general purpose system adequate for Knowledge Discover in Databases (KDD) running in general purpose PCs and based on distributed computing over a connected network of PCs. In this paper we discuss the extension of HARVARD to interact with a Grid Computing setting. This extension, called HARVARD-g, enable the HARVARD system to schedule task to the Grid and therefore largely increase its available computational power. keywords: Grid Computing, Parallel and Distributed Computing.
Spatial NavigatioN iN geometric mazeS: a computatioNal model of rodeNt Behavior

Abstract Navigation is deﬁned as the capability of planning and performing a path from thecurrent position towards a desired location. Diﬀerent types, or strategies, of navigationare used by animals depending on the task they are trying to solve. Visible goals canbe approached directly, while navigation to a hidden goal usually requires a memorizedrepresentation of relative positions of the goal and surrounding landmarks.Neurophysiological and behavioral experiments on rodents suggest that diﬀerent brainareas are responsible for the expression of diﬀerent navigation strategies. Speciﬁcally, dorsalstriatum has been related to storage and recall of stimulus-response associations underlyingsimple goal-approaching behaviors, whereas hippocampus is thought to store the spatialrepresentation of the environment. Such a representation is built during an unrewardedspatial exploration and appears to be employed in cases when simple stimulus-responsestrategies fail. Discovery of neurons with spatially correlated activity, i.e. place cells andgrid cells, in the hippocampal formation complements behavioral and lesion data suggestingits role for spatial orientation.The overall objective of this work is to study the neurophysiological mechanisms under-lying rodent spatial behavior, in particular those that are responsible for the implementationof diﬀerent navigational strategies. Special attention is devoted to the question of how var-ious types of sensory cues inﬂuence goal-oriented behavior.The model of a navigating rat described in this work is based on functional and anatom-ical properties of brain regions involved in encoding and storage of space representation andaction generation. In particular, place and grid cells are modeled by two interconnectedpopulations of artiﬁcial neurons. Together, they form a network for spatial learning, capa-ble of combining diﬀerent types of sensory inputs to produce a distributed representationof location. Goal-directed actions can be generated in the model via two diﬀerent neuralpathways: the ﬁrst one drives stimulus-response behavior and associates visual input di-rectly to motor responses; the second one associates motor actions with places and hencedepends on the representation of location. The visual input is represented by responses ofa large number of orientation-sensitive ﬁlters to visual images generated according to theposition and orientation of the simulated rat in a virtual three-dimensional world.The model was tested in a large array of tasks designed by analogy to experimentalstudies on animal behavior. Results of several experimental studies, behavioral as wells asneurophysiological, were reproduced. Based on these results we formulated a hypothesisabout the inﬂuence that the rat’s perception of surrounding environment exerts on goal-oriented behavior. This hypothesis may provide an insight into several issues in animalbehavior research that were not addressed by theoretical models until now.Keywords: rat, model, navigation, place cells, grid cells, geometry of space, neural net-work, reinforcement learning.
An Automated Algorithm for Fast Pulse Wave Detection

This study presents an automated algorithm for fast pulse wave detection, directed to establishing the presence of cardiac activity in an emergency. The method relies on real- time estimation of similarity of closely positioned rising edges of the waveform and decision logic. The algorithm was tested on a set of pressure pulse waves from the MGH/MF waveform database from PhysioNet. Our approach to assessing the algorithm performance was based on location and classification of suspicious 10 s signal epochs by means of detection of dissimilar peak-to-peak intervals. The detected epochs were visually inspected and compared to the corresponding ECG-based expert beat annotations. The main epoch and error types were summarized. The performance of the algorithm and the visual interpretation of the results were illustrated by means of examples. The review of the recordings showed that the proposed algorithm correctly identifies cardiac pulsations even under considerable artefacts. Our conclusion is that the algorithm reliably detects critical periods in cardiac activity and is applicable to fast pulse wave detection in real-time applications and ambulatory measurement setups.
User space approach to audio device driving on UNIX-like systems

The proposed project attempts to resolve some of the inconvenience factors related to implementing device drivers in kernel space, by using a different approach. With the goals of simplifying development efforts and of maximizing performance, design decisions have been taken in the majority of UNIX-like operating systems to implement their kernels using a monolithic design and to build most device driver subsystems into them. This has a number of drawbacks which this proposal document will elaborate on. The proposed project will attempt to resolve these drawbacks by implementing a driver framework entirely in user space. The proposal is to do this specifically for sound devices because this appears to be a very suitable class of drivers for this kind of experiment.
Long-memory modeling and prediction of automotive active suspension power consumption

This paper considers long-memory modeling and prediction of power consumption in automotive fully-active suspension systems. The study is based upon a novel pseudo-linear method for the estimation of long-memory fractionally integrated ARMA (ARFIMA) models and an experimental active suspension vehicle. In addition to nonstationarity, the power consumption signal is shown to possess long-memory characteristics which are effectively captured by the ARFIMA model structure. Its comparison with alternative model structures indicates its superiority over conventional ARMA/ARIMA approaches. Although its achieved predictive accuracy is somewhat lower than that of fundamentally nonstationary time-dependent ARMA (TARMA) models, the ARFIMA model is shown to achieve satisfactory performance at a drastically reduced parametric complexity, which is lowest among all considered model structures.
Analysis of Link Algorithms for Web Mining

As the use of Web is increasing more day by day, the web users get easily lost in the web's rich hyper structure. The main aim of the owner of the website is to provide the relevant information to the users to fulfill their needs. Web mining technique is used to categorize users and pages by analyzing users behavior, the content of pages and order of URLs accessed. Web Structure Mining plays an important role in this approach. In this paper we discuss and compare the commonly used algorithms i.e. PageRank, Weighted PageRank and HITS.
Low Voltage Ride-through for Doubly Fed Induction Generator Using Battery-Storage System

In this paper, enhanced field oriented control technique (EFOC) was adopted in Rotor Side Control (RSC) of DFIG converter for improved response during severe faults. The work is intended to damp pulsations in electromagnetic torque, improve voltage mitigation and limit surge currents and to enhance the operation of DFIG during voltage sags. The converter topology uses a battery energy storage system with capacitor storage system to further enhance operation of DFIG during faults. The battery and capacitor system in coordination provide additional real and reactive power support during faults and nearly constant voltage profile at stator and rotor terminals and limit overcurrents. For EFOC technique, rotor flux reference changes its value from synchronous speed to zero during fault for injecting current at the rotor slip frequency. In this process DC-Offset component of flux is controlled, decomposition during overvoltage faults. The offset decomposition of flux will be oscillatory in a conventional FOC, whereas in EFOC it will damp quickly. A comparison is made with proposed methodology with battery energy storage system and a conventional system. Later the system performance with under voltage of 50% the rated voltage with fault at PCC during 0.8 to 1.2 seconds is analysed using simulation studies.