Miracle-dz/UniFAR-FTUs huggingface dataset:
Tone Dependent Color Error Diiusion Project Report Multidimensional Dsp, Spring 2003 Vishal Monga

Conventional grayscale error di usion halftoning produces worms and other objectionable artifacts. Tone Dependent error di usion for grayscale halftoning (Li and Allebach) helps reduce these artifacts by controlling di usion of quantization errors based on the input graylevel value. Allebach et al. design error lters weights and thresholds for each (input) graylevel optimized based on a human visual system (HVS) model. In this report we extend tone dependent error di usion to color. A visually optimum deisgn approach for tone dependent error lters (for each color plane) is presented. The resulting halftones are seen to overcome most of the traditional error di usion artifacts.
Multiple video watermarking algorithm based on wavelet transform

Digital watermarking techniques embed information in multimedia, such as image, audio and video. The watermark has been resolved copyright ownership and verifying the integrity of content. This paper proposed a watermarking technique based on wavelet transform and binary watermark. The embedded algorithm has been done in two bands of video. Furthermore, the watermark extraction process does not need the original video sequence. The experimental results show the algorithm is more robust and it can achieve copyright protection and authentication.
Smart Acess Control Model Akash

For century, there has been no change in the fundamental structure of the electrical power grid and vehicle networks. Current hierarchical, centrally controlled grid of the electrical grid is not best for growing demand. To address the challenges of the existing power grid, the new concept of smart grid and smarter planet are under research. The smart grid can be considered as a modern electric power grid infrastructure for enhanced efficiency and reliability through automated control, high-power converters, modern communications infrastructure, sensing and metering technologies, and modern energy management techniques based on the optimization of ondemand, energy and network availability. While current power systems are based on a solid information and communication infrastructure, the new smart grid needs a different and much more complex one, as its dimension is much larger and needs utmost performance. This paper addresses critical issues on smart grid technologies primarily in terms of information and communication technology (ICT) issues and opportunities. The main objective of this paper is to provide a contemporary look at the current state of the art in smart grid communications as well as to discuss the still-open research issues in this field. It is expected that this paper will provide a better understanding of the technologies, potential advantages and research challenges of the smart grid and provoke interest among the research community to further explore this promising research area. KeywordsAdvanced metering infrastructure (AMI), communication technologies, quality-ofservice (QoS), smart grid, standards
Architecture for dynamic range extension of analogue-to-digital conversion

This paper presents a new technique to extend the dynamic range of the signal conversion from the analogue domain to the digital domain. It is based in a new technique to split the incoming analogue signal into two portions and then convert it to the digital domain using for that two parallel analogue-to-digital converters, ADC, and afterwards reconstruct the signal in the digital domain. The proposed architecture will be explained giving more emphasis on the analogue signal splitter. Then, this architecture will be validated through several simulations. We will demonstrate an improvement of around 6 dB in the dynamic range for different signal excitations, in which are included one-tone, two-tones and multisine signals.
Modeling of IEEE 802.11e Contention Free Bursting Scheme with Heterogeneous Stations

Contention free bursting (CFB) is an innovative quality-of-service (QoS) scheme specified in the IEEE 802.11e standard. To reduce the contention overheads, this scheme enables the stations that gain the channel to transmit multiple frames back-to-back in a burst. Most existing analytical models of the CFB scheme have been developed under the assumption of identical stations with saturated traffic loads. In this paper, we present an analytical model for investigating the performance metrics of throughput, end-to-end delay, loss probability, and energy consumption of the CFB scheme in the presence of nonidentical stations with different traffic generation rates. The model is validated against extensive ns2 simulation experiments. Numerical performance results demonstrate the efficiency of the CFB scheme for improving the network performance.
Dynamic localization of G proteins in Dictyostelium discoideum

Extracellular stimuli exert their effects on eukaryotic cells via serpentine G-protein-coupled receptors and mediate a vast number of physiological responses. Activated receptors stimulate heterotrimeric G-proteins, consisting of three subunits, α, β and γ. In Dictyostelium discoideum, cAMP binds to the cAMP receptor cAR1, which is coupled to the heterotrimer containing the Gα2 subunit. These studies provide in vivo evidence as to how receptors influence the localization of the G-protein complex prior to and after ligand binding. Previous work has shown that the state of the heterotrimer could be monitored by changes in fluorescence (or Förster) resonance energy transfer (FRET) between the α2- and β-subunits of D. discoideum. We now report the kinetics of G-protein activation as a loss of FRET prior to and after cAMP addition by using total internal reflection fluorescence microscopy (TIRFM). We also performed photobleaching experiments to measure G-protein recovery times. Our data show that inactive and active G-proteins cycle between the cytosol and plasma membrane. These data suggest that cAR1 activation slows the membrane dissociation (`off') rate of the α2 subunit, while simultaneously promoting βγ-subunit dissociation.
Using Artificial Neural Networks ( ANNs ) and Adaptive Neuro-Fuzzy Inference System ( ANFIS ) for Modeling and Sensitivity Analysis of Effective Rainfall

Effective rainfall is animportantfactor in determiningtheresources ofavailable water for the crops. A proper understanding of effective rainfall can help for efficient management of water resourcesin agricultural systems. In this study, the abilities of some intelligence models including Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for estimation of effective rainfall were investigated. Tabriz synoptic station located in the northwest of Iran was considered as studying area and USDA-SCS method as the most widely used method for calculation of effective rainfall was applied to determine the monthly effective rainfalls, for the period of 1982 to 2010. In the next step,ANFIS and ANNs models were trained and tested for mentioned years and consequently the predictive results of models compared with the results of SCS method. With attention to the results, the accuracies of both models are satisfied, and ANNs showed relatively more accurate results.Considering this fact, that the USDA-SCS method, expresses the rainfall and evapotranspiration as important factors in determining the effective rainfall, ANFIS and ANNs models applied for the sensitivity analysis of evapotranspiration for finding the most effective parameteramong the components of this factor. Results showed thatin Tabriz synoptic station, as a semi-arid region, temperature andrelative humidity –respectively -in combination with rainfall, are the most effective parametersfor determining the effective rainfall. Key-Words: -Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Artificial Neural Networks (ANNs), modeling, effective rainfall, Sensitivity Analysis, USDA-SCS method
Quantum-behaved particle swarm optimization algorithm with Lévy mutated global best position

This paper proposes a novel quantum-behaved particle swarm optimization (QPSO) algorithm with the global best (gbset) position subjected to Levy probability distribution. Firstly, a Gaussian mutation with the mean being the gbest position and the standard deviation being half of the distance between the mean best (mbest) position and gbest position is executed on gbest position, hence, a GGQPSO algorithm is formed. On the basis of it, by selecting a fast and accurate Levy random numbers numerical simulation algorithm and defining the ”effective standard deviation”, the only 2 parameters of Levy distribution can be determined according to empirical equations and trials respectively. Therefore, LGQPSO is taken shape. The distributions of gbest positions in LGQPSO, GGQPSO and QPSO are compared with each other. For the purpose of verifying the effectiveness of LGQPSO, QPSO algorithm with Cauchy mutated gbest position is introduced. The above four algorithms are tested on 5 test functions. It is shown by the experiment results that LGQPSO is an approach that could considerably improve performances of QPSO. LGQPSO is more likely to escape from local optimum and obtain steadier solutions in most cases.
Stability of finite difference models containing two boundaries or interfaces

It is known that the stability of finite-difference models of hyperbolic initial-boundary value problems is connected with the propagation and reflection of parasitic waves. Here the waves point of view is applied to models containing two boundaries or interfaces, where repeated reflection of trapped packets is a potential new source of instability. Our analysis accounts for various known instability phenomena in a unified way and leads to several new results, three of which are as follows: (1) Dissipativity does not ensure stability when three or more formulas are concatenated at a boundary or internal interface, (2) Algebraic ''GKS instabilities'' can be converted by a second boundary to exponential instabilities only when an infinite numerical reflection coefficient is present. (3) ''GKS-stability'' and ''P-stability'' can be established in certain problems by showing that the numerical reflection coefficient matrices have norm less than one.
Build Petroleum Exploration and Production Domain Ontology from Reference Models

The petroleum exploration and production domain includes more than twenty specialties such as exploration, production, etc. Difference information vocabulary among specialties results in problems about information sharing and application software integration among specialties. Ontology was introduced to solve above problem. According the feature of petroleum business, the method to building Petro-Onto(petroleum exploration and production domain ontology) was proposed, the top-level ontology was designed, and an approach was proposed to capture concepts and the relationships among concepts automatically from business models and data models. Petro-Onto has been applied in oil field information integration.
Dept. for Speech, Music and Hearing Quarterly Progress and Status Report Prediction of Formant Frequencies from Linear Combinations of Filterbank and Cepstral Coefficients Prediction of Formant Frequencies from Linear Combinations of Filterbank and Cepstral Coefficients

The topic of this paper is formant frequency prediction using multiple linear regression. This technique provides robust formant frequency estimates but with limited precision. We apply this approach to predict formant frequencies of one male speaker comparing three different spectral representations. Mel scaled filterbanks are shown to perform slightly better than linear prediction based cepstral coefficients. The baseline approach uses one linear prediction model for each formant. This approach is extended by using multiple models for each formant. In this case, each model is trained on data from sub-bands of the formant frequency. This method is shown to be very useful for predicting F2, which has a large frequency span. The rms error of F2 can be reduced by up to 45% using multiple models on an independent test set. Moderate sizes of training data suffice to derive the linear prediction models which implies that predictors can be trained for a new speaker with a small formant labelling effort.
Application of IT in Biomedical Field: Present Status and Future Prospects

In the knowledge economy, information has become the most important resource allowing both firms and nations to grow. For every drug that reaches the market, there are more than 10,000 compounds synthesized, characterized, and tested for biological effects if we go by conventional laboratory procedure. This process takes place in about 12 years. But this task of drug discovery and evaluation is going to carry out at much faster rate and at significantly lower cost with the use of computer based program's. called software. Not only pharmaceutical, biotechnological and biomedical research required software but production, automation of process and its control, clinical diagnosis of disease and clinical trials of anti-AIDS and cancer drug all required some sophisticated software. Overall without use of software there is no simple task is possible in biomedical research. Today, computers are so important in biomedical research and development that it may be hard to imagine a time when there were no computers to assist the researchers. In this article we summarized the use of software in different aspects of biomedical research.
Probabilistic forecasting of shallow, rainfall-triggered landslides using real-time numerical weather predictions

A project established at the National Institute of Water and Atmospheric Research (NIWA) in New Zealand is aimed at developing a prototype of a real-time land- slide forecasting system. The objective is to predict tem- poral changes in landslide probability for shallow, rainfall- triggered landslides, based on quantitative weather forecasts from numerical weather prediction models. Global weather forecasts from the United Kingdom Met Office (MO) Nu- merical Weather Prediction model (NWP) are coupled with a regional data assimilating NWP model (New Zealand Lim- ited Area Model, NZLAM) to forecast atmospheric variables such as precipitation and temperature up to 48 h ahead for all of New Zealand. The weather forecasts are fed into a hy- drologic model to predict development of soil moisture and groundwater levels. The forecasted catchment-scale patterns in soil moisture and soil saturation are then downscaled us- ing topographic indices to predict soil moisture status at the local scale, and an infinite slope stability model is applied to determine the triggering soil water threshold at a local scale. The model uses uncertainty of soil parameters to pro- duce probabilistic forecasts of spatio-temporal landslide oc- currence 48 h ahead. The system was evaluated for a damag- ing landslide event in New Zealand. Comparison with land- slide densities estimated from satellite imagery resulted in hit rates of 70-90%. tralian crustal plates, is characterized by a dynamic active landscape - mountainous and hilly regions with steep slopes cover large areas of the country; uplifted and dissected sedi- mentary and volcanic rocks with low strength properties are
Ultra-high-bandwidth monolithic VCSEL arrays for high-speed metro networks

In this paper, we demonstrate that directly modulated InP short-cavity single-mode VCSEL arrays emitting at 1.55 μm are suitable optical transmitters for metro network applications. We show 400-Gb/s transmission over 400-km standard single mode fiber using a wavelength-division-multiplexed and polarization-division-multiplexed 4-level pulse-amplitude-modulated (4PAM) monolithic VCSEL array, enabled by digital coherent detection.
Relationship between fluorodeoxyglucose uptake in the large vessels and late aortic diameter in giant cell arteritis.

OBJECTIVE GCA carries an increased risk of developing thoracic aortic aneurysms. Previous work with fluorodeoxyglucose (FDG)-PET has shown that the aorta is frequently involved in this type of vasculitis. We wanted to investigate whether there is a correlation between the extent of vascular FDG uptake during the acute phase of GCA and the aortic diameter at late follow-up. METHODS All patients with biopsy-proven GCA who ever underwent an FDG-PET scan in our centre were asked to undergo a CT scan of the aorta. The diameter of the aorta was measured at six different levels (ascending aorta, aortic arch, descending aorta, abdominal suprarenal, juxtarenal and infrarenal aorta) and the volumes of the thoracic and of the abdominal aorta were calculated. RESULTS Forty-six patients agreed to participate (32 females, 14 males). A mean of 46.7 +/- 29.9 months elapsed between diagnosis and CT scan. All aortic dimensions were significantly smaller in women than in men, except for the diameter of the ascending aorta. Patients who had an increased FDG uptake in the aorta at diagnosis of GCA, had a significantly larger diameter of the ascending aorta (P = 0.025) and descending aorta (P = 0.044) and a significantly larger volume of the thoracic aorta (P = 0.029). In multivariate analysis, FDG uptake at the thoracic aorta was associated with late volume of the thoracic aorta (P = 0.039). CONCLUSION GCA-patients with increased FDG uptake in the aorta may be more prone to develop thoracic aortic dilatation than GCA patients without this sign of aortic involvement.
No Effect of Cathodal Transcranial Direct Current Stimulation on Fear Memory in Healthy Human Subjects

Background: Studies have demonstrated that fear memories can be modified using non-invasive methods. Recently, we demonstrated that anodal transcranial direct current stimulation (tDCS) of the right dorsolateral prefrontal cortex is capable of enhancing fear memories. Here, we examined the effects of cathodal tDCS of the right dorsolateral prefrontal cortex during fear reconsolidation in humans. Methods: Seventeen young, healthy subjects were randomly assigned to two groups, which underwent fear conditioning with mild electric stimuli paired with a visual stimulus. Twenty-four hours later, both groups were shown a reminder of the conditioned fearful stimulus. Shortly thereafter, they received either tDCS (right prefrontal—cathodal, left supraorbital—anodal) for 20 min at 1 mA, or sham stimulation. A day later, fear responses of both groups were compared. Results: On Day 3, during fear response assessment, there were no significant differences between the tDCS and sham group (p > 0.05). Conclusion: We conclude that cathodal tDCS of the right dorsolateral prefrontal cortex (right prefrontal—cathodal, left supraorbital—anodal) did not influence fear memories.
Quantization of Multiple Sources Using Nonnegative Integer Bit Allocation

Asymptotically optimal real-valued bit allocation among a set of quantizers for a finite collection of sources was derived in 1963 by Huang and Schultheiss, and an algorithm for obtaining an optimal nonnegative integer-valued bit allocation was given by Fox in 1966. We prove that, for a given bit budget, the set of optimal nonnegative integer-valued bit allocations is equal to the set of nonnegative integer-valued bit allocation vectors which minimize the Euclidean distance to the optimal real-valued bit-allocation vector of Huang and Schultheiss. We also give an algorithm for finding optimal nonnegative integer-valued bit allocations. The algorithm has lower computational complexity than Fox's algorithm, as the bit budget grows. Finally, we compare the performance of the Huang-Schultheiss solution to that of an optimal integer-valued bit allocation. Specifically, we derive upper and lower bounds on the deviation of the mean-squared error (MSE) using optimal integer-valued bit allocation from the MSE using optimal real-valued bit allocation. It is shown that, for asymptotically large transmission rates, optimal integer-valued bit allocations do not necessarily achieve the same performance as that predicted by Huang-Schultheiss for optimal real-valued bit allocations
Twin-Screw Extrusion Processing of Rainbow Trout (Oncorhynchus mykiss) Feeds Using Various Levels of Corn-Based Distillers Dried Grains with Solubles (DDGS)

ABSTRACT Increasing demand for seafood products and rising demand for fish meal for commercial fish feeds is driving the search for effective alternative protein sources. Twin-screw extrusion trials were conducted to study the production of nutritionally balanced feeds for rainbow trout fingerlings (Oncorhynchus mykiss). Six isocaloric (≈4.61 kcal/g) ingredient blends with a target protein content of >45% db were formulated with 0, 10, 20, 30, 40, and 50% distillers dried grains with solubles (DDGS) and other feed ingredients. The moisture contents of the diets were initially adjusted to 5–7% db, and then extruded at 250 rpm using dual 1.9 mm dies with varying amounts of steam (7.2–7.7 kg/hr) injected into the conditioner and water (4.3–6.5 kg/hr) into the extruder. Mass flow rates, moisture contents, and temperatures were measured during processing and moisture content, water activity, unit density, bulk density, expansion ratio, compressive strength, compressive modulus, pellet durability index, water s.
Part-of-Speech tagging based on artificial neural networks

In this paper, we describe a Part-of-Speech tagging system based on connectionist models. A Multilayer Perceptron is used following corpus-based learning from contextual and lexical information. The Spanish corpus LexEsp has been used for the training and evaluation of the tagging system based on artificial neural networks. Different approaches have been compared and results with the Hidden Markov Model systems are also given. The results show that the connectionist approach is feasible.
People-oriented software reuse: the very thought

The authors argue for integrating the role of software engineers into existing reuse paradigms and providing tool support to assist problem description and component understanding, selection and adaptation. Empirical studies indicate that these reuse tasks are difficult, even for experienced software engineers. Therefore, guidelines and a high-level architecture for design of tool support based on reports of behavior and problems arising during reuse are presented. <<ETX>>
A people-to-people recommendation system using tensor space models

Existing recommendation systems often recommend products to users by capturing the item-to-item and user-to-user similarity measures. These types of recommendation systems become inefficient in people-to-people networks for people to people recommendation that require two way relationship. Also, existing recommendation methods use traditional two dimensional models to find inter relationships between alike users and items. It is not efficient enough to model the people-to-people network with two-dimensional models as the latent correlations between the people and their attributes are not utilized. In this paper, we propose a novel tensor decomposition-based recommendation method for recommending people-to-people based on users profiles and their interactions. The people-to-people network data is multi-dimensional data which when modeled using vector based methods tend to result in information loss as they capture either the interactions or the attributes of the users but not both the information. This paper utilizes tensor models that have the ability to correlate and find latent relationships between similar users based on both information, user interactions and user attributes, in order to generate recommendations. Empirical analysis is conducted on a real-life online dating dataset. As demonstrated in results, the use of tensor modeling and decomposition has enabled the identification of latent correlations between people based on their attributes and interactions in the network and quality recommendations have been derived using the 'alike' users concept.