Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelling of Closed Loop Class E Inverter Based Induction Heater

This study presents simulation of class E inverter based induction heater system using simulink. DC is converted into high frequency AC using class E inverter. This high frequency A C is used for induction heating. Closed loop systems are modeled and they are simulated using Mat lab Simulink.The results of closed loop systems are presented. The proposed amplifier with two series-parallel resonant load networks will allow sinusoidal output voltage to be achi eved by associ ating with the positive and negative quasi-sinusoidal waveforms. The complementarily activated configuration will provide continuous high-ripple-frequency input- current waveforms; this approach significantly reduces electromagnetic interference and requires very little filtering. With the symmetry of the push-pull Class-E Circuit, there is the additional benefit tha t the even harmonics are suppressed at the load, and thus there are fewer harmonic distortions.
Adding range restriction capability to dynamic data structures

A database is said to allow range restrictions if one may request that only records with some specified field in a specified range be considered when answering a given query. A transformation is presented that enables range restrictions to be added to an arbitrary dynamic data structure on <italic>n</italic> elements, provided that the problem satisfies a certain decomposability condition and that one is willing to allow increases by a factor of <italic>O</italic>(log <italic>n</italic>) in the worst-case time for an operation and in the space used. This is a generalization of a known transformation that works for static structures. This transformation is then used to produce a data structure for range queries in <italic>k</italic> dimensions with worst-case times of <italic>O</italic>(log<italic><supscrpt>k</supscrpt> n</italic>) for each insertion, deletion, or query operation.
BSW and MSW Students’ Opinions About and Responses to the Death of Freddie Gray and the Ensuing Riots: Implications for the Social Justice Emphasis in Social Work Education

Abstact In April 2015, a Black man, Freddie Gray, died in police custody in Baltimore, Maryland. A day of rioting followed. These events provided the researcher with the opportunity to ascertain social work students’ opinions about and actions in response to these occurrences and their implications for the social justice mission of the social work profession. Students expressed a commitment to social justice and reported that it was emphasized in their education. A significant minority indicated that the Gray case was not addressed in any of their social work classes or in their field practicum. Although respondents reported that the Gray case further enhanced their commitment to the principle of social justice, few engaged in any sort of activity in response to Gray’s death and the unrest either at the time of the events or 6 months later. Implications for social work education are discussed.
Versatile reconfiguration of radiation patterns, frequency and polarization: A discussion on the potential of controllable reflectarrays for software-defined and cognitive radio systems

The application of the reconfigurable reflectarray structure to software-defined and cognitive radio is discussed for the first time. The main idea is that the reflectarray has the potential-in addition to dynamical beamforming-of simultaneous frequency and polarization reconfiguration or diversity/multiplexing gains, at low complexity and cost. We discuss the expected benefits of this potentially universally reconfigurable antenna system in the context of (MIMO) software-defined and cognitive radio. We also deduce the requirements on the controllable reflectarray unit cell phase characteristics and feed antennas, to achieve the desired novel reconfiguration capability.
Discriminative Learning of Receptive Fields from Responses to Non-Gaussian Stimulus Ensembles

Analysis of sensory neurons' processing characteristics requires simultaneous measurement of presented stimuli and concurrent spike responses. The functional transformation from high-dimensional stimulus space to the binary space of spike and non-spike responses is commonly described with linear-nonlinear models, whose linear filter component describes the neuron's receptive field. From a machine learning perspective, this corresponds to the binary classification problem of discriminating spike-eliciting from non-spike-eliciting stimulus examples. The classification-based receptive field (CbRF) estimation method proposed here adapts a linear large-margin classifier to optimally predict experimental stimulus-response data and subsequently interprets learned classifier weights as the neuron's receptive field filter. Computational learning theory provides a theoretical framework for learning from data and guarantees optimality in the sense that the risk of erroneously assigning a spike-eliciting stimulus example to the non-spike class (and vice versa) is minimized. Efficacy of the CbRF method is validated with simulations and for auditory spectro-temporal receptive field (STRF) estimation from experimental recordings in the auditory midbrain of Mongolian gerbils. Acoustic stimulation is performed with frequency-modulated tone complexes that mimic properties of natural stimuli, specifically non-Gaussian amplitude distribution and higher-order correlations. Results demonstrate that the proposed approach successfully identifies correct underlying STRFs, even in cases where second-order methods based on the spike-triggered average (STA) do not. Applied to small data samples, the method is shown to converge on smaller amounts of experimental recordings and with lower estimation variance than the generalized linear model and recent information theoretic methods. Thus, CbRF estimation may prove useful for investigation of neuronal processes in response to natural stimuli and in settings where rapid adaptation is induced by experimental design.
Linear Dispersion Codes Selection Based on Grassmannian Subspace Packing

Subspace Packing Dan Deng, Student Member, IEEE, and Jinkang Zhu, Member, IEEE Abstract: A new method to design codebook is proposed for Linear Dispersion Code (LDC) systems with limited feedback. The extended codebook design method is introduced based on Grassmannian subspace packing and a mapping function, which is derived from the relationship between SVDs of the classical multiplexing systems and the LDC systems. Our proposed codebook can guarantee the maximum min-distance of the finite set. Simulation results show that, for 2×2 MIMO systems, if the feedback bit number is 4, the proposed codebook shows the similar performance to the optimal precoding codebook and has substantial performance advantage against the random search codebook.
Review of locking devices used in robotics

—Locking devices are widely used in robotics, for instance to lock springs, joints or to reconﬁgure robots. This review paper classiﬁes the locking devices currently described in literature and preforms a comparative study. Designers can, as such, better determine which locking device best matches the needs of their application. The locking devices are divided into three main categories based on different locking principles: mechanical locking, friction-based locking and singularity locking. Different lockers in each category can be passive lockers or active lockers. Based on an elaborate literature study, the paper summarizes the ﬁndings by comparing different locking devices, based on a set of properties of a theoretical ideal locking device.
Accelerating Techniques for Rapid Mitigation of Phishing and Spam Emails

Spam filters that are implemented using Naïve Bayesian learning techniques are widely deployed worldwide with email clients such as Outlook®. These filters that are deployed on end user’s computers and typically used to filter out spam for individual users are effective when the spam load is around 400-500 spam emails per day per user. However, when the spam load increases, these solutions prove to be slow and hence insufficient for practical use. In this paper, we identify the computation intensive functions of such machine learning algorithms and solve the performance issues by implementing these functions on hardware. Earlier similar approaches made use of specialized hardware chips or coprocessors to achieve such acceleration. These chips being dedicated hardware represent a cost and scalability limitation. Our approach makes use of a more generic Intel desktop processor, viz. Tolapai (Intel EP80579) that has several built-in cryptographic functionalities, viz. security accelerators for bulk encryption, authentication, hashing and public/private key generation. Experimental results show that significant acceleration can be achieved by migrating some of the functionalities to hardware in a transparent way.
MILK AND BLOOD CONCENTRATIONS OF LIPOPOLYSACCHARIDE-BINDING PROTEIN IN COWS WITH NATURALLY-OCCURRING SUBCLINICAL AND CLINICAL MASTITIS

Title of Document: MILK AND BLOOD CONCENTRATIONS OF LIPOPOLYSACCHARIDE-BINDING PROTEIN IN COWS WITH NATURALLYOCCURRING SUBCLINICAL AND CLINICAL MASTITIS Rongyu Zeng, M.S., 2008 Directed By: Associate professor Brian J. Bequette Department of Animal and Avian Sciences Dr. Douglas D. Bannerman Bovine Functional Genomics Laboratory, BARC,USDA-ARS The objective of this thesis was to evaluate the blood and milk concentrations of lipopolysaccharide-binding protein (LBP) from cows with naturally-occurring mastitis as biomarkers of this disease. Milk and blood samples were collected from 101 clinically healthy dairy cows and 17 dairy cows with clinical mastitis. The concentrations of LBP, haptoglobin and serum amyloid A (SAA) were determined in these samples by enzymelinked immunosorbent assay. Both LBP and haptoglobin concentrations were higher in the milk and blood of quarters and cows with clinical mastitis respectively than in those that were healthy. Whereas haptoglobin concentrations differed between uninfected and subclinically-infected quarters, LBP concentrations only differed between them when milk somatic cell counts were low. Unlike haptoglobin and SAA, blood concentrations of LBP in cows with a subclinical intramammary infection were not significant from those of cows with all healthy quarters. Thus, haptoglobin may be a preferred biomarker of subclinical intramammary infection. MILK AND BLOOD CONCENTRATIONS OF LIPOPOLYSACCHARIDE-BINDING PROTEIN IN COWS WITH NATURALLY-OCCURRING SUBCLINICAL AND CLINICAL MASTITIS
Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting, Lead Times, and Information.: The Impact of Forecasting, Lead Times, and Information.

An important observation in supply chain management, known as the bullwhip effect, suggests that demand variability increases as one moves up a supply chain. In this paper we quantify this effect for simple, two-stage supply chains consisting of a single retailer and a single manufacturer. Our model includes two of the factors commonly assumed to cause the bullwhip effect: demand forecasting and order lead times. We extend these results to multiple-stage supply chains with and without centralized customer demand information and demonstrate that the bullwhip effect can be reduced, but not completely eliminated, by centralizing demand information.
Improper Deep Kernels

Neural networks have recently re-emerged as a powerful hypothesis class, yielding impressive classification accuracy in multiple domains. However, their training is a non-convex optimization problem which poses theoretical and practical challenges. Here we address this difficulty by turning to “improper” learning of neural nets. In other words, we learn a classifier that is not a neural net but is competitive with the best neural net model given a sufficient number of training examples. Our approach relies on a novel kernel construction scheme in which the kernel is a result of integration over the set of all possible instantiation of neural models. It turns out that the corresponding integral can be evaluated in closed-form via a simple recursion. Thus we translate the non-convex, hard learning problem of a neural net to a SVM with an appropriate kernel. We also provide sample complexity results which depend on the stability of the optimal neural net.
An Approach to Image Encryption and Decryption using DFF Transform with Chaos

In recent years, many image cryptosystems are proposed. As encryption process is applied to the whole image, it is difficult to improve the efficiency. Encryption of digital image processing becomes more important for Internet data transportation therefore many methods have been applied for the processing. To focus on security aspect, in this paper we proposed a novel method of image encryption using discrete fractional Fourier transform (DFrFT) with chaos. This technique has been implemented and results are discussed in detail with analyzing parameters like Security, Sensitivity and MSE.
Cluster optimization for improved web usage mining using ant nestmate approach

The web usage mining uses data mining techniques to discover interesting usage patterns from web data. Web personalization uses web usage mining techniques for the process of customization. Customization involves knowledge acquisition done by analysis of user's navigational behavior. A user when goes online would like to get the links which suits his requirements or usage in the website he visits. The next business requirement in the online industry will be personalizing/customizing the web page fulfilling for each individuals requirement. The personalization of the web page will involve clustering of different web pages having common usage pattern. As the size of the cluster goes on increasing due to increase in users or growth of interest of users it will become inevitable need to optimize the clusters. This paper proposes a cluster optimizing methodology based on ants nestmate recognition ability and is used for eliminating the data redundancies that may occur after the clustering done by the web usage mining methods. For clustering an ART1-neural network based approach is used. “AntNestmate approach for cluster optimization” is presented to personalize web page clusters of target users.
Feeler: Emotion Classification of Text Using Vector Space Model

Over the last quarter-century, there is increasing body of research on understanding the human emotions. In this study, automatic classification of anger, disgust, fear, joy and sad emotions in text have been studied on the ISEAR (International Survey on Emotion Antecedents and Reactions) dataset. For the classification we have used Vector Space Model with a total of 801 news headlines provided by “Affective Task” in SemEval 2007 workshop which focuses on classification of emotions and valences in text. We have compared our results with ConceptNet and powerful text based classifiers including Naive Bayes and Support Vector Machines. Our experiments showed that VSM classification gives better performance than ConceptNet, Naive Bayes and SVM based classifiers for emotion detection in sentences. We achieved an overall F-measure value of 32.22% and kappa value of 0.18 for five class emotional text classification on SemEval dataset which is better than Navie Bayes (28.52%), SVM (28.6%). We have tested and discussed the results of classification using cross-validation technique for emotion classification and sentiment analyses on both the ISEAR and SemEval datasets. In addition to the classification experiments we have developed an emotion enabled video player which automatically detects the emotion from subtitle text of video and displays corresponding emoticon.
Reorganizing CAD Assembly Models (as-Designed) for Manufacturing Simulations and Planning (as-Built)

: A CAD assembly model is a collection of components, comprised recursively of various levels of individual parts and sub-assembly models. Although the overall geometry in the CAD assembly model accurately represents the final finished product, this model is often meaningless from a manufacturing planning and simulation point of view. The grouping of the components into sub-assemblies and the assembly sequence implied by the CAD model does not accurately represent the manufacturing/assembly sequence or process. Reordering, regrouping, and modifying existing sub-assemblies, or creating new sub-assemblies in the CAD model may be needed to ensure this correspondence for simulation or assembly planning tools that derive the manufacturing sequence directly from the CAD system. Although this task can be performed using the existing functionality of the CAD system, it quickly becomes unwieldy for large industry-grade models due to the assembly constraints and relationships in place during the model creation. In industry today, there are several digital mockup and assembly planning tools that greatly outstrip the ability of CAD systems in loading complete models and creating assembly plans. Methods to organize the CAD model assembly quickly and easily for use in these systems, and to record the "as manufactured" assembly in the CAD data format are needed to close the loop and ease the transfer, storage, and maintenance of model data between the CAD systems, PDM systems, and these new age planning and mockup systems. In this paper, a new hybrid method is proposed to provide this functionality. Of key significance is the fact that using this approach, polygonal representations of any new or modified sub-assemblies designated in the reorganized hierarchy can be produced and that the original constraints used in the original assembly are transformed in a consistent manner to the new components. In addition, corresponding property files for the new components can be created for use in the assembly planning tool. In order to validate this hybrid approach, the time required to rearrange the assembly hierarchy and output the required information using both methods are compared–1) the traditional method using the CAD system alone, and 2) the new hybrid system. A statistical analysis using three treatment factors indicates that if the number of components is more than 15, then it is far more efficient to use the hybrid method over the CAD system by itself. This hybrid method implementation has now been used very successfully
Optimized assessment of atrial fibrillation organization through suitable parameters of Sample Entropy

Sample Entropy (SampEn) is a nonlinear regularity index that requires the a priori selection of three parameters: the length of the sequences to be compared, m, the patterns similarity tolerance, r, and the number of samples under analysis, N. Appropriate values for m, r and N have been recommended in some cases, such as heart rate, hormonal data, etc., but no guidelines exist for the selection of that values. Hence, an optimal parameters study should be required for the application of SampEn to not previously analyzed biomedical signals. In this work, a thorough analysis on the optimal SampEn parameter values within two different scenarios of AF organization estimation, such as the prediction of paroxysmal AF termination and the electrical cardioversion outcome in persistent AF, is presented. Results indicated that, (i) the proportion between N and the sampling rate (ƒs) should be higher than one second and ƒs ≥ 256 Hz, (ii) overlapping between adjacent N-length windows does not improve organization estimation and (iii) values of m and r maximizing classification should be considered within a range wider than the proposed in the literature for heart rate analysis, i. e. m = 1 and m = 2 and r between 0.1 and 0.25 times the standard deviation of the data.
ORGANIZATIONAL ATTENTION: A METAPHOR FOR A CORE COGNITIVE PROCESS

Organizational attention is an underdeveloped construct that can account for a variety of organizational phenomena. Attention is the means by which individuals select and process a limited amount of input from the enormous amount of internal and environmental inputs bombarding the senses, memories and other cognitive processes. This article develops a coherent theory of organizational attention, drawing on Cornelissenșs domain-interactive metaphor model. Topics that form the building blocks of individual attention research, including selective and divided attention, automatic versus controlled processes, attention and memory, attention and learning, are examined in terms of their applicability to the organizational context.
Scalar Radiative Transfer in Discrete Media with Random Oriented Prolate Spheroids Particles

Monte Carlo scalar radiative transfer simulation of light scattering in plane parallel slab is not a simple problem, especially in the study of angular distribution of light intensity. Approximate phase function such as Henyey-Greenstein is often used to simulate the Mie phase function. But even for sphere particle this function is sometimes a poor approximation of real phase function. For a spheroids particle, the angular scattering characteristics cannot be approximated as H- G phase function with su-cient accuracy. In this paper, we study the transmission characteristics of light in parallel plane layer with randomly oriented prolate spheroids aerosol particles. Instead of using H-G phase function, we use sampling method to simulate real phase function of spheroid directly. A database of phase function with various scattering angle and azimuth angles for given spheroids aerosol particle is developed. The transmission characteristics calculated by scattering phase function sampling method and equivalent volume sphere H-G phase function method are compared. The efiect of prolate spheroids particle size and form factor on optical transmission properties is analyzed. It is found that although the construction database of phase function takes a certain amount of computing time, for spheroid particles the sample phase function method, compared with the H-G phase function simulation method, can greatly improve the accuracy of transmittance calculation.
Smart traffic scheduling in 802.11 WLANs with access point

In distributed systems like 802.11 WLANs, simple requirements like providing fair access to all users are hard to get by because of the random access protocol and the unpredictability of the wireless channel. The main contribution of this paper is the proposal of an LLC-layer algorithm that is implemented at both AP and WSs. The algorithm aims at guaranteeing fair access to the medium to every user, by awarding longer transmission opportunities to WSs that experienced short channel failures. At the same time, an adaptive contention window setting protects downlink traffic from the unbalancement typical of WLANs with AP. The proposed solution is outlined and presents a simulation study, which shows the effectiveness of the new algorithm in comparison to the standard 802.11b implementation both under a TCP and a UDP traffic scenario.
Data Mining using Pruned Artificial Neural Network Tree (ANNT)

Artificial neural network (ANN) has not been effectively utilized in data mining because of the "black box" nature. This issue was resolved by using artificial neural network tree (ANNT) approach in our earlier works. Future improvement was made by incorporating pruning in ANNT approach. ANNT pruning approach consists of three phases: training, pruning and rule extraction. The training phase is concerned with ANN learning followed by pruning. In pruning, the redundant links from the trained network are deleted, rules are extracted from the pruned network. The proposed scheme results in extracting rules from contributing links and indirectly reduces the number of rules but maintaining classification accuracy