Miracle-dz/UniFAR-FTUs huggingface dataset:
Separable Spatio-spectral Patterns in EEG signals During Motor-imagery Tasks

Separable Spatio-Spectral Patterns in EEG signals During Motor-Imagery Tasks Amirhossein Shokouh Aghaei Doctor of Philosophy Graduate Department of Electrical and Computer Engineering University of Toronto 2014 Brain-Computer Interface (BCI) systems aim to provide a non-muscular channel for the brain to control external devices using electrical activities of the brain. These BCI systems can be used in various applications, such as controlling a wheelchair, neuroprosthesis, or speech synthesizer for disabled individuals, navigation in virtual environment, and assisting healthy individuals in performing highly demanding tasks. Motor-imagery BCI systems in particular are based on decoding imagination of motor tasks, e.g., to control the movement of a wheelchair or a mouse curser on the computer screen and move it to the right or left directions by imagining right/left hand movement. During the past decade, there has been a growing interest in utilization of electroencephalogram (EEG) signals for non-invasive motor-imagery BCI systems, due to their low cost, ease of use, and widespread availability. During motor-imagery tasks, multichannel EEG signals exhibit task-specific features in both spatial domain and spectral (or frequency) domain. This thesis studies the statistical characteristics of the multichannel EEG signals in these two domains and proposes a new approach for spatio-spectral feature extraction in motor-imagery BCI systems. This approach is based on the fact that due to the multichannel structure of the EEG data, its spatio-spectral features have a matrix-variate structure. This structure, which has been overlooked in the literate, can be exploited to design more efficient feature extraction methods for motor-imagery BCIs. Towards this end, this research work adopts a matrix-variate Gaussian model for the spatio-spectral features, which assumes a separable Kronecker product structure for the covariance of these features. This separable structure, together with the general properties of the Gaussian model, enables us to design new feature extraction schemes which can operate on the data in its inherent matrix-variate structure to reduce the computational cost of the BCI system while improving its performance. Throughout this thesis, the proposed matrix-variate model and its implications will be studied in various different feature extraction scenarios.
Polymers For Dental And Orthopedic Applications

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Research and Implementation of a Parallel Discrete Event Network Emulator

Internet is characterized as a uncontrollable,variable and unpredictable infrastructure,this brings the difficulty of developing,debugging and evaluating network applications.Network emulation system constructs a virtual network environment which has the characteristics of controllable and repeatable network conditions.This makes it possible to predict the correctness and performance of proposed new technology before deploying to Internet.In this paper we present a methodology for predicting the correctness and performance of applications based on the PARNEM,a parallel discrete event network emulator.PARNEM employs a BSP based real-time event scheduling engine,provides flexible interactive mechanism and facilitates legacy network models reuse.PARNEM allows detailed and accurate study of application behavior.Comprehensive case studies covering bottleneck bandwidth measurement and distributed cooperative web caching system demonstrate that network emulation technology opens a wide range of new opportunities for examining the behavior of applications.
Improved feature selection algorithm with conditional mutual information

Traditional feature selection algorithms have a common drawback, i.e. they do not consider the mutual relationships between features. It can result in that one feature's predictive power is weakened by others and the lost of efficiency. In this paper, we proposed a new feature selection method called Conditional Mutual Information Maximin (CMIM). It can select a set of individually discriminating and weakly dependent features. Simulation results demonstrate that the proposed method can improve the precision of text classification.
Cell Phone Controlled Device

: This paper represents a novel method which enables users to control their home appliances and systems from remote using a cell phone-based interface. To access the control unit, the user should send an authentication code (DTMF) along with the required/desired function/action to his/her home control system via Global System for Mobile communication (GSM). Upon being properly authenticated, the cell phone-based interface at home (control unit) would relay the commands to a microcontroller that would perform the required function/action, and return a function completion code that would be sent to the source of the original command (user‟s cell phone).
Framework for Tracking the Event-Based Evolution in Social Networks

This paper presents a fundamentally different framework for uncovering the intricate properties of evolutionary networks.Contrary to static snapshots methods,this paper first traces the timelines of the networks. Then,based on extracted smooth segments from the timelines,a graph approximation algorithm is applied to capture the frequent characteristics of the network and reduce the noise of interactions.Moreover,by employing the relationship among multi-attributes,an innovative community detection algorithm is proposed for a detailed analysis on the approximate graphs.To track these dynamic communities,this paper also introduces a community correlation and evaluation method.Finally,by applying this novel framework to several real-world networks,this paper demonstrates the critical relationship between event and social evolution,and reveals meaningful properties in actual dynamic behaviors.
Robust discrete geodesic and its applications in digital geometry processing

Geodesic is a fundamental concept of Differential Geometry; it measures the length of the shortest path along the surface between two points. Geodesic has many important properties such as being a distance metric, locally isotropic, invariant to isometric transformation. Facilitated by these advantages, geodesic plays an important role in many applications, ranging from computer graphics, digital geometric processing to computer vision and image processing, etc. Computing discrete geodesics on meshes have been extensively studied since 1980s. A number of efficient methods to compute discrete geodesics on polygonal meshes have been presented, allowing us to efficiently compute the discrete geodesics on large-scale real-world models. However, in spite of its popularity, geodesics are highly sensitive to local geometric and topological noises, which seriously diminish their application. Introducing arbitrary small topological changes (e.g., holes, gaps, small handles) might cause arbitrarily large change in geodesic distance between pair of points. Therefore, to obtain robust and accurate geodesics, the input meshes are usually required to be free of noise and with good tessellation. However, this requirement may not be met for real-world models obtained from 3D scanning, a careful preprocessing (e.g., hole/gap filling, denoising, remeshing, etc) must be done before applying the discrete geodesic algorithms. Thus, it is highly desirable to develop an efficient and robust method to compute geodesics on defect models. This thesis aims at developing robust discrete geodesic algorithms to address the challenges of computing geodesics on the defect models. Our contributions include: 1) The first contribution is an intrinsic algorithm for computing the meaningful approximate geodesics on polygonal meshes with holes. Without the explicit hole filling, our
Automatic Procedure for Monitoring Urban Land Cover Changes by Multitemporal ASAR Images

In this paper an automatic procedure for change detection from multitemporal advanced synthetic aperture radar (ASAR) images is presented. The model is structured in four main blocks: a set of standard preprocessing procedures, a feature-extraction module, a neural-network classifier and a change detection module [1]. To improve the capabilities of the neural network, the feature-extraction module derives a set of features from a series of multitemporal ASAR images, such as mean and standard deviation of four backscattering intensity images (one per each season of the year) and their textural features (Energy and Contrast) based on the Gray Level Co-Occurrence Matrix (GLCM) method [2,3]. The supervised neural network algorithms [4,5] is used to classify urban areas versus not urban areas. It is based on the multilayer perceptron MLP approach and trained with the error back-propagation (EBP) learning algorithm. Finally, the change detection map is obtained by comparing two classified images taken at two different times. To validate the model, test images acquired by the ENVISAT ASAR over Rome, Italy and its surroundings are used. Changes occurred from 2005 to 2009 are automatically recognized with an accuracy of about 75%, while the classification accuracy obtained over the urban areas is higher than 90%. The developed algorithm is capable of efficiently identifying urban growth on big scale areas with short processing time.
Forced Alignment Under Adverse Conditions

The problem of forced alignment is that of matching phonetic segments in an audio sample to its corresponding transcription, which is a vital part of indexing audio files. While various methods have been employed to accomplish this task, the results become less accurate under adverse alignment conditions caused by various disturbances in the audio as well as transcription errors. In fact, the alignment errors are usually left undiscovered until the aligned audio and transcript combination is later reviewed by human eyes and ears, thus defeating the purpose of an automated transcription and alignment process. This project seeks to develop a robust method to improve existing forced alignment techniques and increase their functionality. This will be accomplished by developing a technique to detect errors in alignment and produce correction algorithms to reduce the frequency of these errors. Various methods to find and fix errors in the alignment process will be examined. By combining these different techniques, a more accurate forced alignment package will be generated, which will be able to operate in adverse conditions found in both the transcript and audio.
Test-bank Design and Algorithm of Test System Based on JAVA

This paper designs a subsystem of test-bank in the testing system based on JAVA.The subsystem employs split design scheme while its test paper generating section adopts two algorithms: extraction algorithm and an algorithm based on shuffle algorithm.This paper also discusses the test-bank design plan and the design process of test paper generation algorithm based on extraction of questions,and presents corresponding codes.The system has a strong versatility and replicability.
The function of Ras proteins in Dictyostelium discoideum

The Ras family of GTP-binding proteins has been implicated in the control of cellular growth, differentiation and motility. They are molecular switches which are active when bound to GTP and inactive when GDP-bound. Dictyostelium cells express five different Ras family members. Ras activation is mediated by guanine nucleotide exchange factors (RasGEFs) which facilitate exchange the of GDP for GTP by Ras proteins. The identification and molecular genetic characterisation of novel Dictyostelium RasGEFs is the main subject of this thesis. The discovery of a physiological function for the Dictyostelium RasD protein is also described. I have identified five partial cDNAs from the Tsukuba Dictyostelium cDNA project which have strong sequence homology to known RasGEFs. To investigate the function of these proteins, Dictyostelium cells with single gef gene disruptions were generated by homologous recombination. Four of the five mutants appeared to grow and develop normally but one mutant, gefB-, showed growth and developmental abnormalities. gefB- cells are unable to proliferate in axenic culture and are impaired in phagocytosis and fluid-phase endocytosis. Conversely, gefB- cells show an enhanced rate of migration, moving twice as fast as wild-type cells. The mutant cells have abnormal morphology; they are highly polarised, have many elongated microspikes and have an absence of pinocytic crowns on the cell surface. In addition, gefB- cells show a cell autonomous impairment in multicellular development. Although the phenotype of vegetative gefB- cells is similar to that of rasS- cells, it was not possible to show a direct interaction between RasS and GefB. However, GefB does exhibit the catalytic properties of a RasGEF in vivo, since it is able to complement the temperature sensitive cdc25-5 S.cerevisiae mutant. The Dictyostelium RasD protein is a small GTP-binding protein closely related to the mammalian Ras proteins Ha-, Ki- and N-Ras, and is maximally expressed during the multicellular stage of Dictyostelium development. Previous work had predicted that RasD was essential for correct differentiation and pattern formation in Dictyostelium aggregates. To further investigate the function of RasD, Dictyostelium cells containing a disrupted rasD- gene were generated by homologous recombination. Surprisingly, rasD- cells proliferate, aggregate and develop indistinguishably from wild-type cells. However rasD slugs exhibit a clear defect in phototaxis and thermotaxis exhibiting an approximately thirty-fold decrease in the efficiency of orientation towards a light or heat source relative to wild-type slugs. RasD is the first signalling protein shown to be necessary for phototaxis in Dictyostelium slugs.
A Novel Timing Estimation Method for OFDM Systems over Multipath Fading Channels

In orthogonal frequency division multiplexing (OFDM) system, the properties of cyclic prefix (CP) energy difference and set correlation are not destroyed at the back of CP in multipath fading channels. Optimum combined with the two merits, a new time synchronization method is proposed in this paper. Simulation results demonstrate that the proposed method has good timing estimation performance over multipath fading channels. And the proposed method is robust to the frequency offset and the performance will be further improved with the increasing of Doppler frequency shift.
Emissions of Nitrous Oxide and Methane in a Subtropical Ferralsol Subjected to Nitrogen Fertilization and Sheep Grazing in Integrated Crop-Livestock System

Brazilian agriculture contributes significantly to nitrous oxide (N2O) and methane (CH4) emissions, so the understanding of such emissions at the field is crucial for mitigation strategies. This study quantified the impact of N application and sheep grazing on the N2O and CH4 emissions from a subtropical Ferralsol under an integrated crop-livestock (ICL) management system. In a long-term experiment in southern Brazil, gaseous fluxes were measured during a year-long cycle of ryegrass (Lolium multiflorum) plus oats (Avena sativa) winter pasture and a summer crop of common beans (Phaseolus vulgaris). Three rates of urea-nitrogen (0, 75, and 150 kg ha) were applied to the winter-pasture, which was subjected to two sheep grazing levels (continuously grazed, and ungrazed). The experiment had a complete randomized block design with three replicates. Soil N2O and CH4 fluxes were measured with closed static-chambers (0.20 m high × 0.25 m in diameter). Nitrous oxide emission peaks occurred 28 days after N application and increased with N application rate. Accordingly, the cumulative N2O emissions averaged across grazed and ungrazed treatments increased from 0.45 kg ha in the control soil to 1.78 and 2.10 kg ha after application of 75 and 150 kg ha, respectively. The N2O emission factors were 1.7 and 1.1 % when the N rates were 75 and 150 kg ha, respectively. The cumulative average N2O emission for all N rates was 2.09 kg ha in ungrazed pasture, but it was reduced by 62 % with grazing to 0.80 kg ha, perhaps because of a possible denitrification of N2O to N2 associated with soil compaction from trampling. Overall, fertilizer-N is an important source of N2O from soil under ICL based on sheep grazing, with emission factors consistent with the IPCC’s default of 1 % (0.3-3.0 %). Grazing reduced the emission of soil N2O, but the underlying cause of that reduction needs to be better understood.
Trivalent Rare Earth Ion Conduction in the Rare Earth Tungstates with the Sc2(WO4)3-Type Structure

To realize a trivalent ion conduction in solids, the Sc2(WO4)3-type structure was chosen on the basis of the mobile trivalent ions and the structure which reduces the electrostatic interaction between the framework and the mobile trivalent ionic species as much as possible. The typical conductivity of the rare earth tungstates R2(WO4)3 (R = Sc, Y, and Er−Lu) with the Sc2(WO4)3-type structure was found to be on the order of 10-5 S cm-1 at 600 °C. Among the rare earth tungstates, Sc2(WO4)3 (σ600°C = 6.5 × 10-5 S cm-1, Ea = 44.1 kJ mol-1) was found to be the most suitable size for the ionic conduction with regard to the relation between the mobile ion radius and the lattice size. The rare earth ion conducting characteristics were investigated by means of the rare earth concentration cell measurements and dc electrolyses. The electromotive force measurements with the Sc−Y binary alloy and the yttrium tungsten bronze as the electrodes strongly suggest the possibility of the trivalent ion conduction of rare ear.
The Achievement of GPS Data-Acquisition Software

Based on C#. NET and Superstar II OEM board,the GPS data-acquisition software has been developed.This software can collect satellite number,space distribution and signal intensity of every visual satellite,positioning result in the form of north latitude,east longitude,height above sea level,error factor such as HDOP,PDOP and VDOP,NMEA-0183 and collected data files,dynamically.It is useful to analyze GPS receiver performance,Confirming the period of GPS data acquisition,and the gathering place for the optimal positioning results relating to the disposal of data.
Fuzzy Evaluation of Web Services Quality Based on Tri-dimensional QoS Model

To assist the quality evaluation application of Web services in different phases of whole lifecycle,a tri-dimensional quality of service model is provided,and a few key quality indexes are defined.Based on the QoS model and fuzzy algorithm in fuzzy mathematics,a Web services fuzzy quality evaluation approach is introduced.Also,a prototype is given to verify such a model and evaluation approach.Experimental results support the approach and prove its feasibility and applicability.
Dynamic P2P trust model based on time-window feedback mechanism

The trust models employed by the existing systems do not provide adequate support to cope with dishonest feedbacks,strategically altering behaviors of malicious peers efficiently and the incentive mechanism.The time-window based local trust model TW-Trust was presented to solve these problems.It considers the inherent connection among trust,reputation and incentive and the effect of time factor on the trust computation.Six trust parameters are introduced to compute trustworthiness of peers,named direct trust,history reputation,difference reputation,misusing reputation,simi-larity and incentive utility.These parameters are adjusted in time to reflect the dynamic of the trust environment using feedback control mechanism,thus,the trust evaluation has better adaptability to the dynamic of trust.Theoretical analysis and experimental results show that the TW-Trust has advantages in countering strategic altering behavior and dishonest feedbacks of malicious peers and solving the sparseness issue in the computation of similarity.It is more effective in trust computation error and transaction failure rate than the previous models.
The Secure of Ad Hoc Networks

Ad Hoc networks are a new wireless networking paradigm for mobile hosts.Unlike traditional mobile wireless networks,Ad Hoc networks do not rely on any fixed infrastructure.Instead,hosts rely on each other to keep the networks connected.The military tactical and other security-sensitive operations are still the main applications of Ad Hoc networks,although there is a trend to adopt Ad Hoc networks for commercial uses due to their unique properties.One main challenge in design of these networks is their vulnerability to Ad Hoc networks attacks.In this paper,we study the threats an Ad Hoc network faces and the security goals to be achieved.We identify the new challenges and opportunities posed by this new networking environment and explore new approaches to secure its communication.In particular,we take advantage of the inherent redundancy in Ad Hoc networks-multiple routes between nodes-to defend routing against denial of service attacks.We also use replication and new cryptographic schemes,such as threshold cryptography,to build a highly secure and highly available key management service,which forms the core of our security framework.
Group Growing Based Small-World Scale-Free Network Model

Recent work has shown that small-world and scale-free are both the important characters of many large scale cotnplex networks. Building network models with these characters is essential to study the topology and behavior of networks. Based on the study of existed network models, a GGSS(Group Growing based Small-world Scale-free) network model is presented. Strong local connectivity and network evolving by group and the preferential attachment rule are combined in this model. Networks generated using this model have small-world and scale-free simultaneous- ly. Simulation is done to verify the correctness of GGSS model.
Detecting Anomalies by Online Techniques Using Spam Detection

In data mining and machine learning anomaly detection has been an important research topic. Intrusion or credit card fraud detection is the many real world applications are require effective and efficient frameworks to identify deviated data instances. Most anomaly detection methods are typically implemented in batch mode, and thus cannot be easily extended to large-scale problems without sacrificing computation and memory requirements. An online oversampling principal component analysis is an existing algorithm to address this problem. The proposed system consists of a spam detection method to detect the spam data in the user login. This approach is used to analyses the data with same text. Using stopping words and stopping terms methods the spam data's are secured with the algorithm as effective and efficient in the Networks.
CUDA DSP Filter for ECG Signals

Real time processing is very important and critical for analysis of ECG signals. Prior to each processing, the signal needs to be filtered to enable feature extraction and further analysis. In case of building a data processing center that analyzes thousands of connected ECG sensors, one expects that the signal processing needs to be done very fast. In this paper, we focus on parallelizing the sequential DSP filter for processing of heart signals on GPU cores. We set a hypothesis that a GPU version is much faster than the CPU version. In this paper we have provided several experiments to test the validity of this hypothesis and to compare the performance of the parallelized GPU code with the sequential code. Assuming that the hypothesis is valid, we would also like to find what is the optimal size of the threads per block to obtain the maximum speedup. Our analysis shows that parallelized GPU code achieves linear speedups and is much more efficient than the classical single processor sequential processing.