Miracle-dz/UniFAR-FTUs huggingface dataset:
THE KALMAN FILTER IN ACTIVE NOISE CONTROL

Most Active Noise Control (ANC) systems use some form of the LMS [5][7] algorithm due to its reduced computational complexity. However, the problems associated with it are well-known, namely slow convergence and high sensitivity to the eigenvalue spread [3][7]. To overcome this problems the RLS algorithm is often used, but it is now widely known, that the RLS loses many of its good properties for a forgetting factor lower than one. Namely, it has been shown that in some application the LMS algorithm is actually better in tracking non-stationary signals than the RLS algorithm [2][3]. One approach, which is works well with non-stationary signals, is to use some specialized form of the Kalman filter, which can be interpreted as a generalization of the RLS algorithm [1][3][4]. The Kalman filter has a high computational complexity, similar to that of the RLS algorithm, which can make it costly for some applications. Nevertheless for narrowband ANC, the number of taps is not very large [7] and the application of the Kalman filter in ANC may be easily handled by today DSP's. In fact, in multi-channel applications the Kalman gain can be computed only once for all the error signals, so the complexity hardly increases with a moderated number of those. Even in the cases where it can not be easily implemented in real time, it can still be used as a benchmark. In fact the Kalman filter is optimal for a given system model [1]. In this paper, specialized version of the Kalman filter fitted to ANC is developed both for primary and secondary path modeling. It is shown throw computer experiments that a large reduction in the residual noise can be achieved in non-stationary environments, compared with the LMS and RLS based algorithms, specially with on-line secondary path modeling.
Crowd Density Estimation Based on ELM learning algorithm

Crowd density estimation in public areas with people gathering and waiting is the important content of intelligent crowd surveillance . A real-time and high accuracy algorithm is necessary to be inputted in the classification and regression of crowd density estimation to improve the speed and increase the efficiency. Extreme Learning Machine (ELM) is a neural network architecture in which hidden layer weights are randomly chosen and output layer weights determined analytically. In this paper, we propose a new method which is based on Haralick’s texture vectors, Gray-level Co-occurrence Matrix and ELM . The datasets are based on PETS2009 and UCSD. The performances are compared among SVM, BP and ELM. The experimental results suggest that the ELM learning algorithm has a good performance of accuracy and a very fast speed than other methods.
GPU-Cuts : Combinatorial Optimisation , Graphic Processing Units and Adaptive Object Extraction GPU-Cuts : Segmentation d ’ Objects par Optimisation Combinatoire sur Processeur Graphique

Object extraction is a core component of computer vision with application to segmentation, tracking, etc. In this paper we propose a GPU graph-based approach to object segmentation. The main contributions of our approach consist of an adaptive, evolving schema to update to statistical properties of the object and the use of a local variant push-relabel algorithm to recover the global minimum of the designed cost function. Furthermore, we propose the implementation of the method on a graphics processing unit where each node of the graph is considered as an independent processor that is connected with the neighborhood nodes. Such a schema recovers segmentation in an optimal and progressive manner. Promising experimental results and important decrease on the computational complexity demonstrate the potentials of our approach.
1337 Examination of a Two-Factor Bond Option Valuation Model

In this paper, we examine a two-factor option pricing model, that could be used within a system to manage the total interest rate position of a financial institution. We deduce the theoretical features that are required for such a model and we discuss issues related to the implementation of the model. In particular, the problem of adapting the model to the current market information is addressed. The results of an empirical study within the market of German interest rate warrants show that the prediction quality of the model is considerable. R & U d Dans cet article, nous examinos un modkle h deux facteurs pour 1’Cvaluation d’options, qui peut servir comme une partie d’un systkme pour contr8ler le risque d’int6ts total d’une institution financikre. Nous deduisons les qualitCs thbriques, qui sont nhssaires pour des modtles pareils et nous discutons des probltmes d’emploi. En particulier nous nous occupons de l’adaptation du modkle A la courbe des taux d’int6rets actuelle. Les rCsultats d’une Ctude empirique sur le march6 allemand montrent la qualit6 du modkle.
Study 1 : Associations , Participation and Government : Linking Local Communities and State Actors in Sustainable Rural Development WDR 2003 Background Paper

In both indus trial and developing countries, local associations and community groups have become increasingly important actors in environment and sustainable development. Recent research suggests that the level of connectedness or linkage across organizations and sectors has major impacts on the performance, inclusiveness and sustainability of such associations. Drawing on the results of a recent multi-country research project on associations and civil society, we suggest these linkages are fostered by the action of diverse social and political agents at broader scales. In marginal rural areas of developing countries, local government has a particularly critical role to play in fostering crosscutting linkages and enabling the participation of poor and vulnerable groups.
Hippocampal Expression Analyses Reveal Selective Association of Immediate-Early, Neuroenergetic, and Myelinogenic Pathways with Cognitive Impairment in Aged Rats

Although expression of some genes is known to change during neuronal activity or plasticity, the overall relationship of gene expression changes to memory or memory disorders is not well understood. Here, we combined extensive statistical microarray analyses with behavioral testing to comprehensively identify genes and pathways associated with aging and cognitive dysfunction. Aged rats were separated into cognitively unimpaired (AU) or impaired (AI) groups based on their Morris water maze performance relative to young-adult (Y) animals. Hippocampal gene expression was assessed in Y, AU, and AI on the fifth (last) day of maze training (5T) or 21 d posttraining (21PT) and in nontrained animals (eight groups total, one array per animal; n = 78 arrays). ANOVA and linear contrasts identified genes that differed from Y generally with aging (differed in both AU and AI) or selectively, with cognitive status (differed only in AI or AU). Altered pathways/processes were identified by overrepresentation analyses of changed genes. With general aging, there was downregulation of axonal growth, cytoskeletal assembly/transport, signaling, and lipogenic/uptake pathways, concomitant with upregulation in immune/inflammatory, lysosomal, lipid/protein degradation, cholesterol transport, transforming growth factor, and cAMP signaling pathways, primarily independent of training condition. Selectively, in AI, there was downregulation at 5T of immediate-early gene, Wnt (wingless integration site), insulin, and G-protein signaling, lipogenesis, and glucose utilization pathways, whereas Notch2 (oligodendrocyte development) and myelination pathways were upregulated, particularly at 21PT. In AU, receptor/signal transduction genes were upregulated, perhaps as compensatory responses. Immunohistochemistry confirmed and extended selected microarray results. Together, the findings suggest a new model, in which deficient neuroenergetics leads to downregulated neuronal signaling and increased glial activation, resulting in aging-related cognitive dysfunction.
Statistical Analysis of Packet Interarrival Times in Wireless LAN

This paper is focused on the statistical analysis of packet arrival process in wireless LAN. The approach is fully empirical, and is based on large traces, captured from real wireless LAN. A variety of models for the packet interarrival times distribution are examined. In addition to Q-Q plot and Cramer-von Mises type statistics, are used to evaluate the models. Our findings, different from those of packet interarrival times obtained in wired backbone LAN calculations, indicate that general distribution are not applicable for wireless LAN packet arrival process. Another result of our analysis was that packet interarrival times are long-range dependent. Therefore, traffic models that assume independent exponentially distributed packet interarrival times may not produce reliable results if applied to performance analysis of wireless LAN.
Chapter 9. Large-scale Biogeochemical–physical Interactions in Elemental Cycles

Why is the distribution of most chemicals not uniform within the ocean (Fig. 9.1)? In the absence of sources and sinks the effect of ocean circulation and mixing would be to smooth the distribution of chemicals. An important reason for nonuniformity is the increase in concentration that occurs in regions of evaporation, and the reduction in concentration that occurs in regions of rainfall or river input at the ocean atmosphere interface. Evaporation, precipitation, and river input lead to concentration ranges usually well below 10%, as evidenced in the oceanic variability of salinity. However, as Fig. 9.1 shows, many chemicals have concentration ranges much greater than this. The concentration of inorganic nitrogen, for example, is more than five times smaller near the surface than in the deep ocean. Dissolved inorganic carbon shows a surface depletion that is also substantially larger than the 10% variation that could be induced by freshwater fluxes. The primary mechanism that drives such large concentration gradients is the use of dissolved inorganic chemicals by living organisms for formation of organic matter
An Empirical Framework for Objective Testing for P-Consciousness in an Artificial Agent

Two related and relatively obscure issues in science have eluded empirical tractability. Both can be directly traced to progress in artificial intelligence. The first is scientific proof of consciousness or otherwise in anything. The sec- ond is the role of consciousness in intelligent behaviour. This document approaches both issues by exploring the idea of using scientific behaviour self-referentially as a benchmark in an objective test for P-consciousness, which is the relevant critical aspect of consciousness. Scientific behaviour is unique in being both highly formalised and provably critically de- pendent on the P-consciousness of the primary senses. In the context of the primary senses P-consciousness is literally a formal identity with scientific observation. As such it is intrinsically afforded a status of critical dependency demonstrably no different to any other critical dependency in science, making scientific behaviour ideally suited to a self-referential sci- entific circumstance. The 'provability' derives from the delivery by science of objectively verifiable 'laws of nature'. By exploiting the critical dependency, an empirical framework is constructed as a refined and specialised version of existing propositions for a 'test for consciousness'. The specific role of P-consciousness is clarified: it is a human intracranial cen- tral nervous system construct that symbolically grounds the scientist in the distal external world, resulting in our ability to recognise, characterise and adapt to distal natural world novelty. It is hoped that in opening a discussion of a novel ap- proach, the artificial intelligence community may eventually find a viable contender for its long overdue scientific basis.
Constraint-based control of sensor-based robot systems with uncertain geometry

This short paper presents control schemes for constraintbased task specification of general sensor-based rigid-body robot systems in the presence of geometric uncertainty. The task constraints are expressed using auxiliary coordinates. The uncertain geometric parameters are also expressed using auxiliary coordinates. Based on these expressions control laws are derived to satisfy the task constraints. A state estimator estimates the unknown geometric parameters using the sensor measurements. This paper is a follow-up of previous work [1], which presents a constraint-based framework, defines particular sets of auxiliary coordinates to express the task constraints and the geometric uncertainty, but only works out a velocity-resolved control scheme. While this paper provides the details for acceleration-resolved control schemes, both in case of a soft and a rigid environment, the presented control schemes do not depend on a particular choice of auxiliary coordinates.
Time-Varying FIR Equalization for MIMO Transmission over Doubly Selective Channels

We propose time-varying FIR equalization techniques for spatial multiplexing-based multiple-input multiple-output (MIMO) transmission over doubly selective channels. The doubly selective channel is approximated using the basis expansion model (BEM), and equalized by means of time-varying FIR filters designed according to the BEM. By doing so, the time-varying deconvolution problem is converted into a two-dimensional time-invariant deconvolution problem in the time-invariant coefficients of the channel BEM and the time-invariant coefficients of the equalizer BEM. The timevarying FIR equalizers are derived based on either the matched filtering criterion, or the linear minimum mean-square error (MMSE) or the zero-forcing (ZF) criteria. In addition to the linear equalizers, the decision feedback equalizer (DFE) is proposed. The DFE can be designed according to two different scenarios. In the first scenario, the DFE is based on feeding back previously estimated symbols from one particular antenna at a time. Whereas, in the second scenario, the previously estimated symbols from all transmit antennas are fed back together. The performance of the proposed equalizers in the context of MIMO transmission is analyzed in terms of numerical simulations.
Performance Analysis of Subspace Clustering Algorithms in Biological Data

Data clustering which is also called as Cluster Analysis is the unsupervised classification of data into various clusters. Clustering is a method of unsupervised learning which is generally implemented by various machine learning techniques. In this paper a specific comparison of three kinds of clustering is introduced and finally the cost function and loss function are calculated. For evaluating any clustering methods, calculation of the error percentage of the concerned method play an important factor. In this paper comparison is carried out on k-means clustering algorithms, hierarchical algorithms and density based algorithms. The main criteria where focus is given while comparing the clustering algorithms are: Scalability, Classes for dealing with noise and extra deposition, different dimensions of high levels etc.
A qualitative estimation of the influence of transport factors on the work efficiency

In this paper we make a qualitative estimation of the influence of transport factors on the work efficiency (labour yield). There will be three classes of influence: positive influence, no influence and negative influence. We take into account some transportation factors like the level of noise during the transport, the level of temperature (the thermic comfort), etc. We use a software simulated feed forward neural network in order to solve this classification problem. The network will divide the input data (values for the transport factors) into these three classes. The input data are stored in an XML file, and they are obtained from a questionnaire that was completed by people working in automotive industry. The results are both useful for the transport companies and for the industry managers.
Moderate impact of ERPS on management accounting: a lag or permanent outcome?

The currently most advanced administrative corporate IT solutions have emerged in the form of Enterprise Resource Planning Systems (ERPS). The number of adopters of this new integrated information technology is increasing rapidly. Still, we know very little about the practical consequences these new systems have for managerial accounting and control, and thereby corporate management. The purpose of this study is to explore the effects of integrated, enterprise-wide information systems on management accounting and management accountants' work. Data for the analysis were gathered through a field study of ten companies (e.g., ABB and Nokia) that have experience of integrated information systems in Finland. Our findings indicate that so far, the ERPS projects have led to relatively small changes in management accounting and control procedures. Also, in most of the cases, advanced management accounting techniques - and many of the traditional ones too (e.g., annual budgeting) - are operated in separate systems. For management accountants, ERP-systems have in some cases left more time for analysis instead of routine tasks. The reasons for these findings are discussed and ideas for further research are presented.
Artificial neural network controllers for biped robot

This paper presents comparison of three artificial neural network controllers design based on cascade-forward, feed-forward neural network and radial basis neural network to control level walking of biped robot. The biped robot consists of a hip, knee and ankle of both legs and torso. It uses the experimental flexion angle data of seven-link movements of human for level walking. The simulation environment contains a model of the robotic leg dynamics and different neural networks for inverse dynamics of leg. It has three independent neural networks of three joints separately in order to achieve the level walking. The simulation work is carried out in Matlab. The results showed that the radial basis neural network is better and can be used to control level walking of a biped robot.
Clustered network coordinated beamforming for cooperative spectrum sharing of multiple secondary systems

One of the key challenges in cognitive radio is the problem of spectrum sharing. All sharing techniques proposed in the literature try to utilize the spectrum in the most efficient way, but they either lack on computational simplicity or on spectral efficiency. Network MIMO can be seen as a very promising method for spectrum sharing, because it enables multiple systems to use the same spectrum simultaneously, but requires high processing power. This paper proposes a novel cooperative technique for spectrum sharing between multiple secondary cognitive radio systems. The method, named Network Coordinated Beamforming with user Clustering (NCBC), is based on Network MIMO and utilizes Network Coordinated Beamforming (NCBF) and User Clustering (UC). The proposed NCBC approach decreases the computational complexity of NCBF making it suitable for real time spectrum sharing in cognitive radio. Monte Carlo simulations are carried out to examine the performance of the proposed technique in terms of Signal to Interference Ratio (SIR) and channel correlation. The results depict the superior performance of NCBC compared to a simple Frequency Division Spectrum Sharing (FDSS) technique in terms of aggregate system capacity and sum rate.
Simulating Interdomain Traffic at the Flow-Level

This paper presents a model for interdomain traffic simulation at the flow level. The model explicitly takes into account three network-related variables for the traffic generation process: the arrival process of the layer-4 flows, the traffic volume carried over these flows and the rate at which these flows can send traffic. The variables of the model are first analyzed on a real flow-level interdomain traffic trace to obtain an accurate picture of the properties they must conform to, and which will allow to generate simulated traffic patterns that have statistical properties close to the ones of a true flowlevel interdomain traffic trace. We validate the simulated flow-level traffic patterns by studying the arising of the self-similarity in the traffic, and comparing our results with already known facts about the packet-level traffic self-similarity. The implications of these simulations on the simulation and modeling of interdomain traffic at the flowlevel are also discussed. Simulating Interdomain Traffic at the Flow-Level Steve Uhlig July 2001 Abstract This paper presents a model for interdomain traffic simulation at the flow level. The model explicitly takes into account three network-related variables for the traffic generation process: the arrival process of the layer-4 flows, the traffic volume carried over these flows and the rate at which these flows can send traffic. The variables of the model are first analyzed on a real flow-level interdomain traffic trace to obtain an accurate picture of the properties they must conform to, and which will allow to generate simulated traffic patterns that have statistical properties close to the ones of a true flowlevel interdomain traffic trace. We validate the simulated flow-level traffic patterns by studying the arising of the self-similarity in the traffic, and comparing our results with already known facts about the packet-level traffic self-similarity. The implications of these simulations on the simulation and modeling of interdomain traffic at the flowlevel are also discussed.This paper presents a model for interdomain traffic simulation at the flow level. The model explicitly takes into account three network-related variables for the traffic generation process: the arrival process of the layer-4 flows, the traffic volume carried over these flows and the rate at which these flows can send traffic. The variables of the model are first analyzed on a real flow-level interdomain traffic trace to obtain an accurate picture of the properties they must conform to, and which will allow to generate simulated traffic patterns that have statistical properties close to the ones of a true flowlevel interdomain traffic trace. We validate the simulated flow-level traffic patterns by studying the arising of the self-similarity in the traffic, and comparing our results with already known facts about the packet-level traffic self-similarity. The implications of these simulations on the simulation and modeling of interdomain traffic at the flowlevel are also discussed.
Failures and possibilities of epistemic justice, with some implications for higher education

ABSTRACT This papers draws on the compelling example of a political movement in South Africa in the 1960s and 1970s to explore both how epistemic justice conditions of possibility and of failure play out in practice. It provides a springboard to understand how and why failures of epistemic justice matter tremendously for democratic and inclusive lives and how the historical example can point us in the direction of higher education as a space for Amartya Sen’s redressable injustices if underpinned by Miranda’s Fricker’s core capability of epistemic contribution being available pedagogically to all. The paper engages with ideas, practices and actions fostered by Black Consciousness against apartheid as both a hermeneutical and a testimonial injustice in South Africa, with both having a relational structure of giving and receiving, as Fricker argues and as Jose Medina elaborates by extending the structure to include the communicative and participatory. The paper then shows the importance of these conceptual frames to transformative higher education practices and how such practices might contribute to more epistemic justice in higher education.