Miracle-dz/UniFAR-FTUs huggingface dataset:
Data Analysis Using Regression and Multilevel/Hierarchical Models

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Sparsity in Linear Predictive Coding of Speech

This thesis deals with developing improved techniques for speech coding based on the recent developments in sparse signal representation. In particular, this work is motivated by the need to address some of the limitations of the wellknown linear prediction (LP) model currently applied in many modern speech coders. In the first part of the thesis, we provide an overview of Sparse Linear Prediction, a set of speech processing tools created by introducing sparsity constraints into the LP framework. This approach defines predictors that look for a sparse residual rather than a minimum variance one with direct applications to coding but also consistent with the speech production model of voiced speech, where the excitation of the all-pole filter can be modeled as an impulse train, i.e., a sparse sequence. Introducing sparsity in the LP framework will also bring to develop the concept of high-order sparse predictors. These predictors, by modeling efficiently the spectral envelope and the harmonics components with very few coefficients, have direct applications in speech processing, engendering a joint estimation of short-term and long-term predictors. We also give preliminary results of the effectiveness of their application in audio processing. The second part of the thesis deals with introducing sparsity directly in the linear prediction analysis-by-synthesis (LPAS) speech coding paradigm. We first propose a novel near-optimal method to look for a sparse approximate excitation using a compressed sensing formulation. Furthermore, we define a novel re-estimation procedure to adapt the predictor coefficients to the given sparse excitation, balancing the two representations in the context of speech coding. Finally, the advantages of the compact parametric representation of a segment of speech, given by the sparse linear predictors and the use of the reestimation procedure, are analyzed in the context of frame independent coding for speech communications over packet networks.
Crossover experiments

Crossover designs and within‐subject designs are designs where each experimental unit receives a series of treatments over time. Because treatments are assigned at different time points, one must worry about whether there is carryover or residual effects from a treatment given in a previous period to a subsequent period. This article introduces crossover designs and discusses the statistical analysis of crossover designs. When one has more than two periods and/or more than three treatments, then special designs can be obtained where one can measure a direct comparison between the treatments even if carryover effects exist. Copyright © 2010 John Wiley & Sons, Inc.
A CLUSTER ANALYSIS AND DECISION TREE HYBRID APPROACH IN DATA MINING TO DESCRIBING TAX AUDIT

In this research, we use clustering and classification methods to mine the data of tax and extract the information about tax audit by using hybrid algorithms K-MEANS, SOM and HAC algorithms from clustering and CHAID and C4.5 algorithms from decision tree and it produce the better results than the traditional algorithms and compare it by applying on tax dataset. Clustering method will use for make the clusters of similar groups to extract the easily features or properties and decision tree method will use for choose to decide the optimal decision to extract the valuable information from samples of tax datasets? This comparison is able to find clusters in large high dimensional spaces efficiently. It is suitable for clustering in the full dimensional space as well as in subspaces. Experiments on both synthetic data and real-life data show that the technique is effective and also scales well for large high dimensional datasets
Sensitivity analysis for input vector in multilayer feedforward neural networks

The derivative matrix, or the Jacobian matrix, of the output vector with respect to the input vector is obtained for multilayer feedforward neural networks (MFNNs). This matrix represents the sensitivity to small perturbations in the input of an MFNN. The expression for the matrix describes the performance of the MFNN, such as the generalization capabilities, as well as error-correcting properties. Analysis shows how these aspects of performance are affected by the weight matrices, the sigmoid functions, and the number of layers and nodes of the network. Suggestions are made for the design of MFNNs with good generalization and error-correction. <<ETX>>
Embed Links to External Websites in Videos

Recently, many video sharing sites provide external links so that their video or audio contents can be embedded into external web sites. For example, users can copy the embedded URLs of the videos of YouTube and post the URL links on their own blogs. Clearly, the purpose of such function is to increase the distribution of the videos and the associated advertisement. Does this function fulfill its purpose and what is the quantification? In this paper, we provide a comprehensive measurement study andanalysis on these external links to answer these two questions. With the traces collected from two major video sharing sites, YouTube and Youku of China, we show that the external links have various impacts on the popularity of the video sharing sites. More specifically, for videos that have been uploaded for eight months in Youku, around 15% of views can come from external links. Some contents are densely linked. For example, comedy videos can attract more than 800 external links on average. We also study the relationship between the external links and the internal links. We show that there are correlations; for example, if a video is popular itself, it is likely to have a large number of external links. Another observation we find is that the external links usually have a higher impact on Youku than that of YouTube. We conjecture that it is more likely that the external links have higher impact for a regional site than a worldwide site.
Unlearning algorithm in associative memories: eigenstructure method

Unlearning capabilities are incorporated into a synthesis procedure for a class of discrete-time neural networks. The proposed technique increases storing capacity while maximizing the domain of attraction of each desired pattern to be stored. Making use of learning, forgetting, and unlearning capabilities, networks generated by the method advanced herein are capable of learning new patterns as well as forgetting learned patterns without the necessity of recomputing all the interconnection weights and external inputs. The unlearning algorithm developed is then utilized to equalize the basins of attraction for each desired pattern to be stored in a given network, and to minimize the number of spurious states. Examples are given to illustrate the strengths and weaknesses of the methodologies. <<ETX>>
Forward and backward RLS-DDCE processing in MIMO-OFDM spatial multiplexing receivers

Abstract. In this paper we present a novel approach in frequency domain channel estimation technique. Our proposal is based on the recursive least squares (RLS) algorithm combined with the decision making process called decision directed channel estimation (RLS-DDCE). The novelty and key concept of this technique is the block-wise causal and anti-causal RLS processing that yields two independent processing of RLS along with the associated decisions. Due to the implemented low density parity check (LDPC) code the receiver operates with soft information, which enables us to introduce a new modification of the Turbo principle as well as simple addition of the a-posteriori log-likelihood ratios (LLRs). Although the computational complexity is increased by both of our approaches, the latter is relatively less complex than the earlier. Simulation results show that these implementations outperform the simple RLS-DDCE algorithm and yield lower bit error rates (BER) and more accurate channel information.
A Graphics Processing Unit Implementation and Optimization for Parallel Double‐Difference Seismic Tomography

Abstract Double‐difference seismic tomography can estimate velocity structure and event locations with high precision, but its high‐computation cost along with large memory usage prevents the use of a personal computer to process very large datasets and requires a long‐computation time. This work proposes graphics‐processing‐unit‐ (GPU)‐based acceleration schemes to run the algorithm on a personal computer for very large datasets more efficiently. Generally, the algorithm can be divided into five major steps: input, ray tracing, matrix construction, inversion, and output. This work focuses on accelerating the ray‐tracing and inversion steps, which take almost two‐thirds of the computation time. Before ray tracing, our algorithm preprocesses the data by sorting all recorded event–station paths according to their lengths. Therefore, those path estimation jobs assigned to GPU cores are suitable for the GPU architecture. Furthermore, our work also minimizes the usage of global and local memory to reduce the GPU computing time needed to handle a very large dataset. In addition to parallelizing the inversion computation, our work proposes a GPU‐based elimination method to reduce redundant computation in inversion for further acceleration. In our test, the proposed acceleration schemes can gain maximum speed‐up factors of 31.17 and 35.46 for ray tracing and inversion, respectively, in our test. Overall, the GPU‐based implementation can reach a maximum of 5.98 times faster than the central processing unit‐based implementation.
Intrinsic functional architecture predicts electrically evoked responses in the human brain

Adaptive brain function is characterized by dynamic interactions within and between neuronal circuits, often occurring at the time scale of milliseconds. These complex interactions between adjacent and noncontiguous brain areas depend on a functional architecture that is maintained even in the absence of input. Functional MRI studies carried out during rest (R-fMRI) suggest that this architecture is represented in low-frequency (<0.1 Hz) spontaneous fluctuations in the blood oxygen level-dependent signal that are correlated within spatially distributed networks of brain areas. These networks, collectively referred to as the brain's intrinsic functional architecture, exhibit a remarkable correspondence with patterns of task-evoked coactivation as well as maps of anatomical connectivity. Despite this striking correspondence, there is no direct evidence that this intrinsic architecture forms the scaffold that gives rise to faster processes relevant to information processing and seizure spread. Here, we demonstrate that the spatial distribution and magnitude of temporally correlated low-frequency fluctuations observed with R-fMRI during rest predict the pattern and magnitude of corticocortical evoked potentials elicited within 500 ms after single-pulse electrical stimulation of the cerebral cortex with intracranial electrodes. Across individuals, this relationship was found to be independent of the specific regions and functional systems probed. Our findings bridge the immense divide between the temporal resolutions of these distinct measures of brain function and provide strong support for the idea that the low-frequency signal fluctuations observed with R-fMRI maintain and update the intrinsic architecture underlying the brain's repertoire of functional responses.
Comparing Multi-Objective Approaches for Air Route Planning in Hostile Environments

Route planning for aircraft that should fly in hostile environmentscan be regarded as a multi-objective optimization problem, where the route should enable the aircraft to accomplish its mission tasks with a minimum risk exposure and minimum fuel consumption. This work compares different approaches for multi-objective route planning that have been suggested in the literature regarding their formulation of objectives as well as how they handle the decision maker’s preferences. It is concluded that most route planners minimize threat exposure and route length, but can also include altitude and flight dynamics constraints. Preferences regarding the objectives can be included in the route planning algorithms with weights or priorities. An alternative approach is that the route planner suggests a number of routes and thereafter lets the decision maker select the best one.
Effective Handling of Low Memory Scenarios in Android using Logs

Android memory management unit also plays vital role in great success of android among other operating systems. Memory is a very limited resource especially in embedded systems like phones and tablets. Android contains modified Linux kernel for handling low memory scenarios and other issues. Out Of Memory (OOM) killer in linux kills some of the processes in low memory scenarios. This report gives details on different set of problems in OOM killer. Activity Manager Service(AMS), Low Memory Killer(LMK) in android kills some of the applications in low memory scenarios along with OOM killer. In low memory scenarios we should not kill user interesting or user frequently accessing applications. If we kill any user frequently accessing applications, then we need to load those applications, if user wants to access those applications in future. Load operation is costly, it takes nearly 3-5 sec to load an application, as it varies from one application to others. So if we can predict user interesting or future accessing applications using user log history, we can reduce number of applications gets killed in a period of time. We developed AppsLogger application, which predicts the user interesting applications using log history. We modified traditional AMS source code in android 4.2, such that AMS do not kills user interesting applications received from AppsLogger. We are succeeded to reduce x% number of application gets killed in a period of time using log history as compared with traditional approach. We are succeeded to reduce avg response time of accessing any application as y% , z% in log based exponential,linear approaches as compared with traditional android approach.
Light-weight clustering techniques for short text answers in human computer collaborative (HCC) CAA

We first explore the paedogogic value, in assessment, of questions which elicit short text answers (as opposed to either multiple choice questions or essays). Related work attempts to develop deeper processing for fully automatic marking. In contrast, we show that light-weight, robust, generic Language Engineering techniques for text clustering in a human-computer collaborative CAA system can contribute significantly to the speed, accuracy, and consistency of human marking. Examples from real summative assessments demonstrate the potential, and the inherent limitations, of this approach. Its value as a framework for formative feedback is also discussed.
Integrated digital modulator and analog front-end for GSM digital cellular mobile radio system

An integrated circuit for the GSM (Group Special Mobile) digital mobile radio system which performs the GMSK (Gaussian minimum shift keying) modulation and the analog front-end functions is presented. The circuit was fabricated using a 2- mu m, double-poly, double-metal CMOS process. Its peak dissipation is 180 mW with a single 5-V supply. The die size is 6.7 mm*5.3 mm. The integrated circuit can be employed both in base and mobile stations, between the baseband digital processing and the 900-MHz radio frequency sections. The circuit performance fully meets GSM specifications. <<ETX>>
Development of a human thermal model and its applications for thermographic diagnosis

A mathematical model has been developed for the numerical analysis of thermal conditions within the human body. The model is based on a numerical calculation of bio-heat equation and a mathematical model of a thermoregulation system. The internal temperatures, heat fluxes, and blood temperatures of all segments are calculated simultaneously. We try to apply the model to clinical diagnosis by using thermography images. This paper describes the fundamental analysis of the calculated results and the experimental results. The calculated results compared rather well with the experimental results in some thermal environmental conditions.
Magpick: an Augmented Guitar Pick for Nuanced Control

This paper introduces the Magpick, an augmented pick for electric guitar that uses electromagnetic induction to sense the motion of the pick with respect to the permanent magnets in the guitar pickup. The Magpick provides the guitarist with nuanced control of the sound that coexists with traditional plucking-hand technique. The paper presents three ways that the signal from the pick can modulate the guitar sound, followed by a case study of its use in which 11 guitarists tested the Magpick for five days and composed a piece with it. Reflecting on their comments and experiences, we outline the innovative features of this technology from the point of view of performance practice. In particular, compared to other augmentations, the high temporal resolution, low latency, and large dynamic range of the Magpick support a highly nuanced control over the sound. Our discussion highlights the utility of having the locus of augmentation coincide with the locus of interaction.
Defect-level Inspection Aids for Automated Vessel Visual Inspection

Ship accidents and near misses, with the corresponding risk of crew and passengers injuries and fatalities, environmental pollution, damage or total loss of the ship and its equipment, can be frequently attributed to the failure of structures and machinery. In order to enhance ships safety and avoid unexpected service disruptions, the EC-FP7 INCASS project pursues the integration in a Decision Support System of inspection data collection approaches using robots and specific sensors, together with a risk evaluation, analysis and management framework comprising ship structures, machinery and equipment. This paper focuses on one of the robotic platforms, and on a set of inspection data processing tools, all developed as a sort of toolbox oriented towards automated visual inspection of ships (which was actually initiated within the framework of the prior EC-FP7 MINOAS project). We first describe a multi-rotor based aerial platform which is used to collect images from the surfaces under inspection, either focusing on providing access to remote areas or for broader and intensified data acquisition. Next, we introduce an image stitching software aiming at enhancing the presentation of the visual data collected. Finally, we focus on defect detection with a lightweight image saliency-based detector tuned for searching for generic defects, what can be useful to either guide the image capture onto relevant areas from the inspection point of view or to guide specific crack and corrosion detectors, also developed within the context of the MINOAS/INCASS projects.
Virtual primitives for the representation of features and objects in a remote telepresence environment

This thesis presents the development of a set of novel graphical tools Known as 'virtual primitives' that allow the user of a stereoscopic telepresence system to actively and intuitively model features in a remote environment. The virtual primitives provide visual feedback during the model creating process in the form of a dynamic wireframe of the primitive overlaid and registered with the real object. The operator can immediately see the effect of his decisions and if necessary make minor corrections to improve the fit of the primitive during its generation. Virtual primitives are a generic augmented reality (AR) tool and their applications extend past the modelling of a workspace for telerobot operation to other remote tasks such as visual inspection, surveying and collaborative design. An AR system has been developed and integrated with the existing Surrey Telepresence System. The graphical overlays are generated using virtual reality software and combined with the video images. To achieve a one-to-one correspondence between the real and virtual worlds the AR system is calibrated using a simple pinhole camera model and standard calibration algorithm. An average RMS registration error between the video and graphical images of less than one framegrabber pixel is achieved. An assessment of a virtual pointer confirms that this level of accuracy is acceptable for use with the virtual primitives. The concept of the virtual primitives has been evaluated in an experiment to model three test objects. The results show that using a virtual primitive was superior in accuracy and task completion time to using a pointer alone. Finally, a case study on the remote inspection of sewers demonstrates the advantages of virtual primitives in a real application. It confirms that the use of virtual primitives significantly reduces the subjective nature of the task, offers an increase in precision by an order of magnitude over conventional inspection methods, and provides additional useful data on the characteristics of the sewer features not previously available.
Arabic Opinion Mining Using Combined Classification Approach

In this paper, we present a combined approach that automatically extracts opinions from Arabic documents. Most research efforts in the area of opinion mining deal with English texts and little work with Arabic text. Unlike English, from our experiments, we found that using only one method on Arabic opinioned documents produce a poor performance. So, we used a combined approach that consists of three methods. At the beginning, lexicon based method is used to classify as much documents as possible. The resultant classified documents used as training set for maximum entropy method which subsequently classifies some other documents. Finally, k-nearest method used the classified documents from lexicon based method and maximum entropy as training set and classifies the rest of the documents. Our experiments showed that in average, the accuracy moved (almost) from 50% when using only lexicon based method to 60% when used lexicon based method and maximum entropy together, to 80% when using the three combined methods.
Webcoct – a tool for producing web-based courses

The aim of the paper is to describe the project WeBCoCT (Web Based Courses Creating Tools) intended to become a learning environment to help academic teachers to introduce web-based courses. WeBCoCT has as its main goal to support authors who do not have any experience with the preparation of web based materials. Some examples of courseware developed with WeBCoCT are provided.
Probabilistic integration of audiovisual information to localize sound source in human-robot interaction

This paper proposes a method to estimate a sound source position by fusing the auditory and visual information with Bayesian network in human-robot interaction. We firstly integrate multi-channel audio signals and a depth image about the environment to generate a likelihood map for sound source localization. However, this integration, denoted by "MICs", does not always lead to locate a sound source correctly. For correcting the failure in localization, we integrate the likelihood values generated from "MICs" and the skin-color distribution in an image according to the result of classifying audio signal into speech/non-speech categories. The audio classifier is based on the support vector machine(SVM) and the skin-color distribution is modeled with GMM. With the evidences given by MICs, SVMs and GMM, we infer whether pixels in images correspond to sound source or not according to the trained Bayesian network. Finally, experimental results are presented to show the effectiveness of the proposed method.