Miracle-dz/UniFAR-FTUs huggingface dataset:
Microcontroller Based MPPT Photovoltaic System under Partial Shading Conditions Using Particle Swarm Optimization Algorithm

This Paper presents Maximum Power Point Tracking (MPPT) of Photovoltaic Array under partial shading condition. The power available at the output of photovoltaic cells keeps changing with solar insolation and ambient temperature because photovoltaic cells exhibit a nonlinear current voltage characteristic. A good number of publications report on different MPPT techniques for PV system most of the existing schemes are unable to extract maximum power from the PV array under partial shading conditions. This paper proposes PSO algorithm to track the global power peak under partially shaded conditions. The Proposed MPPT algorithm is developed in Low cost C2000TM PiccoloTM Launch PadTM, LAUNCHXL-F28027 microcontroller. All the observations and conclusions, including results are presented.
Multi-camera based Images through Feature Points Algorithm for HDR Panorama

With the spread of various kinds of cameras such as digital cameras and DSLR and a growing interest in high-definition and high-resolution images, a method that synthesizes multiple images is being studied among various methods. High Dynamic Range (HDR) images store light exposure with even wider range of number than normal digital images. Therefore, it can store the intensity of light inherent in specific scenes expressed by light sources in real life quite accurately. This study suggests feature points synthesis algorithm to improve the performance of HDR panorama recognition method (algorithm) at recognition and coordination level through classifying the feature points for image recognition using more than one multi frames.
Classification of High Dimensional Signals with Small Training Sample Size with Applications towards Microwave Based Detection Systems

Classification techniques attempt to resolve the problem of categorizing data into two or more classes. The data distribution is therefore the most critical fact to be aware of. Unfortunately, specifications of data generators are not available in real life and a probabilistic density parameterisation is not always applicable, especially for the situation of High Dimensional data with Low (training) Sample Size (HDLSS). This raises the importance of developing data driven techniques, where the data model is assumed according to partially accessible prior knowledge or cross-validation. There are various popular data assumptions, such as centroid-based models, linear subspace models, manifold data structures, etc, and one should take into consideration the model accuracy, computational complexity, generalization ability, and be aware of possibilities of overfitting. When the dimensionality of the data is much higher than the training sample size, all issues appear as its nature and there is no easy way to find a good trade-off. In this work, we mainly focus on the first two types of data models and develop corresponding classification techniques. The first objective is to automatically learn the data generating model with limited amount of training samples available. With the assumed data model, the second step is to maximize the class separability with respect to the model assumption. The applications studied encompass both simulated and measured microwave signals for stroke type diagnostics and wood quality assessment. The results are analyzed and compared with more classical approaches.
IoT-Based Cost Saving Offloading System for Cellular Networks

: Nowadays, with the new techniques available in hardware and software, data requests generated by applications of mobile devices have grown explosively. The large amount of data requests and their responses lead to heavy trafﬁc in cellular networks. To alleviate the transmission workload, ofﬂoading techniques have been proposed, where a cellular network distributes some popular data items to other wireless networks, so that users can directly download these data items from the wireless network around them instead of the cellular network. In this paper, we design a Cost Saving Ofﬂoading System (CoSOS), where the Internet of Things (IoT) is used to undertake partial data trafﬁc and save more bandwidth for the cellular network. Two types of algorithms are proposed to handle the popular data items distribution among users. The experimental results show that CoSOS is useful in saving bandwidth and decreasing the cost for cellular networks.
Shaderlib: Uma Biblioteca Para Desenvolvimento e Aprendizado de Shaders com GLSL

Nas ultimas decadas, alem de vivenciarmos uma enorme evolucao da velocidade computacional, ocorreu o surgimento das GPUs programaveis, dando origem a linguagens como GLSL e CUDA. Isto proporcionou uma grande flexibilidade na utilizacao do hardware e atraiu muitos interessados. Mais recentemente, o lancamento do OpenGL 4 proveu ainda mais flexibilidade para os usuarios, porem tornou a curva de aprendizado mais ingreme, ja que mesmo aplicacoes basicas demandam o conhecimento de muitos conceitos de Algebra Linear e proficiencia em programacao em Shaders e OpenGL. Este artigo apresenta uma biblioteca de shaders que visa auxiliar os estudantes, pesquisadores e programadores na utilizacao do OpenGL 4.
Web Page Noise Removal - A Survey

Web mining is used to extract useful information from websites which includes web documents and hyperlinks of web sites. The World Wide Website contains a wide range of web pages which are very useful to many users. Web pages are composed of different kinds of data, such as text, audio, video and images. In addition to this, nowadays, web pages contain a large amount of unnecessary data, e.g., advertisement posters, navigation bars and disclaimer/copyright notices. These types of unnecessary data are called as noisy data. This has created the distractions to the user and also increases the time to perform searches and browsing tasks. To perform in-depth analysis of web data or web content mining, the first and essential step is to remove the noises which are existing in the web pages, and then we can extract useful information from the web pages. Removing noise from the web page is challenging task in web content mining. This main objective of this paper is to discuss the basics of web content mining, types of noises, techniques used for noise removal and different models used in the literature.
Content Based Message Filtering System forOSN User Walls

Online Social Network plays a vital role in our each day life. A person can communicate with others by sharing information in several forms like image, audio and video contents. Major matter in OSN (Online Social Network) is to provide security in posting unnecessary messages. One fundamental question in present Online Social Networks (OSNs) is to give users the capability to have power over the messages posted on their individual private space to avoid that unnecessary content is displayed. Till today , OSNs contribute slight support to this need. As a solution to this problem in this paper, we propose a system in which OSN users to have a straight control on the contents displayed on their private space. This is done with the support of a flexible constrain-depended system, that permits users to provide the filtering to be applied to their walls, and a Machine Learning-based classifier automatically labelling messages in support of content-based filtering
An Efficient and Provably-Secure Broadcast Authentication Scheme in Wireless Sensor Networks

Broadcast authentication is a security mechanism in Wireless Sensor Networks (WSNs), which enables a lot of entities to join in and disseminate the messages into the networks in an authenticated way. Hitherto, some symmetric key based broadcast authentication schemes such as μTESLA and its variants have been proposed to provide such services for wireless sensor networks. Unfortunately, these schemes suffer from serious Denial of Service (DoS) attack due to the time delay in message authentication. In this paper, we propose a certificateless signature based broadcast authentication scheme without bilinear pairing in wireless sensor networks. Our proposal is proved to be secure in the random oracle model under the hardness assumption of discrete logarithm problem. Compared with the previous protocols, the perform evaluations show that this scheme is efficient and appropriate to be applied in wireless sensor networks.
Modeling Unrestricted Coreference in OntoNotes

The CoNLL-2011 shared task involved predicting coreference using OntoNotes data. Resources in this field have tended to be limited to noun phrase coreference, often on a restricted set of entities, such as ACE entities. OntoNotes provides a large-scale corpus of general anaphoric coreference not restricted to noun phrases or to a specified set of entity types. OntoNotes also provides additional layers of integrated annotation, capturing additional shallow semantic structure. This paper briefly describes the OntoNotes annotation (coreference and other layers) and then describes the parameters of the shared task including the format, pre-processing information, and evaluation criteria, and presents and discusses the results achieved by the participating systems. Having a standard test set and evaluation parameters, all based on a new resource that provides multiple integrated annotation layers (parses, semantic roles, word senses, named entities and coreference) that could support joint models, should help to energize ongoing research in the task of entity and event coreference.
Model simplification and validation with indirect structure validity tests

Model simplification distils essential model structures that cause selected problems and increases the quality and understanding of models. It can also be a step towards building theory-like structures and general representations of case-specific problems in various application domains. In this paper, simplification of a large system dynamics model and validation of the simplified version are illustrated. The original model represents agricultural and environmental problems of irrigation development in southeast Turkey and consists of 14 model sectors and 110 stock variables. Its simplified version with a narrow model boundary and higher level of aggregation is a general representation of its selected dynamics and consists of four model sectors and 17 stock variables only. Analysis of reference behaviours, indirect structural validity tests (structure-oriented behaviour tests) and scenario runs reveal the simplified model as a valid and useful simplification of the original one. Copyright © 2006 John Wiley & Sons, Ltd. Syst. Dyn. Rev. 22, 241–262, (2006)
Exploring the correspondence between languages for machine translation

The central topic of this thesis is the exploration of properties that are common across languages and properties that differentiate them, in the context of machine translation. The field of machine translation aims to render the content expressed in one language into another language. Therefore, the difficulty of this task is proportional to how different the given two languages are in expressing the same information. In the first part of the thesis, we focus on properties common across languages. We aim to validate the hypothesis that every language has a level of representation which is shared, to some extent, by all languages. This level of representation is the syntactic structure of a sentence. Consequently, one can narrow down the search for translation correspondences between units of any two languages. We realize these ideas in the form of bilingual syntactic language models which are used as soft constraints during the translation process. The models provide improvements in translation quality in a series of rescoring and decoding experiments. In the second part of the thesis, we use the idea that despite the observation that all natural languages share certain ways of expressing content, there are also many features that differentiate them. We hypothesize that the differences between languages are systematic. We design ensembles of neural machine translation systems sharing the target language but differing in their source languages. We perform a comparison to monolingual ensembles obtained by initializing systems with different random seeds and observe systematically better performance for the multi-source ensembles.
A novel rendering approach for unstructured light field interpolation

In this paper, a novel framework is proposed for light ﬁeld reconstruction from a sparse set of views. The light ﬁeld is the un-structured, and has the large disparity. We indicate that the reconstruction can be eﬃciently modeled as angular restoration on epipolar plane image (EPI). Based on “blur-restoration-deblur” framework, we analyze the EPI with Fourier transform. What’s more, a novel rendering approach is presented by combining proposed “blur-restoration-deblur” framework and depth information to handle large disparity and unstructured light ﬁeld. We evaluate our approach on several datasets and demonstrate the high performance and robustness of the proposed framework compared with the state-of-the-arts algorithms.
Bases de dados alternativas para websites

In the last decade has reigned the use of relational databases. However, in recent years, with the emergence of new technologies, have been created or renewed ideas to achieve greater speed and efficiency. This is why the idea of non-relational databases was reused, resulting in new platforms. Such platforms have been increased visibility for its greater speed and implementation in services such as Facebook and Twitter. With the resurgence of discussion of the advantages over the use of non-relational databases, it is important to realize that these are a viable alternative and efficient for relational databases. Being a new movement, there is no sustained study on the advantages and disadvantages of using these, there are a few studies on its implementation and results, but nothing that validates what has been said on such databases. This dissertation will be well targeted to verify the gains of using a non-relational management system databases for the studied platforms, and will not be an exhaustive study of the differences between relational and non-relational. Initially we tried to understand what is the best management system database to existing platforms Imoguia and Casa Sapo, as well as for its implementation. To ensure a fair comparison it was necessary to migrate existing data to the new database, to ensure that they’re working with the same volume of data. After implementation, comparative tests were carried out in order to determine which system has the best performance to the platforms studied.
Acoustic Event Detection in Multichannel Audio Using Gated Recurrent Neural Networks with High‐Resolution Spectral Features

Recently, deep recurrent neural networks have achieved great success in various machine learning tasks, and have also been applied for sound event detection. The detection of temporally overlapping sound events in realistic environments is much more challenging than in monophonic detection problems. In this paper, we present an approach to improve the accuracy of polyphonic sound event detection in multichannel audio based on gated recurrent neural networks in combination with auditory spectral features. In the proposed method, human hearing perception‐based spatial and spectral‐domain noise‐reduced harmonic features are extracted from multichannel audio and used as high‐resolution spectral inputs to train gated recurrent neural networks. This provides a fast and stable convergence rate compared to long short‐term memory recurrent neural networks. Our evaluation reveals that the proposed method outperforms the conventional approaches.
“iBeacon” technology that will make possible Internet of Things

In 2013 Apple introduced a new technology, called iBeacon. This one is on the basis of the project and in this project, we are going to explain what it is and how it works, in order to understand how the app works and how it uses these potentialities. This new technology may also be used by Android system. An IOS disposal or any other hardware is able to receive push notification from nearby iBeacon disposals through this technology. iBeacon works with Bluetooth Low Energy (BLE), also known as Bluetooth 4.0 or intelligent Bluetooth. We show in this project how iBeacon technically really work, and three possible App examples which were created to better demonstrate and explain how this technology works: The distance app, The proximity demo app and notify The demo app. To conclude we show the differences between iBeacons and NFC followed by some data on the spread of " Internet of Things " and some personal considerations.
Resolution-independent detail preservation for 3D shape editing

We present a new method for detail-preserving 3D shape editing that decouples the complexity of the modeling algorithm from the geometric complexity of the object being edited. A deformation graph, which can be constructed automatically or designed by hand, parameterizes the allowable shape deformations as a collection of affine transformations acting on local regions of the model. Shape editing is formulated as a nonlinear optimization problem where detail preservation is encoded directly by specifying that the affine transformations should be rotations. Editing constraints are formulated on the surface itself, which hides the underlying deformation model and allows the user to select and position arbitrary vertices. Our reduced deformable model can handle complex non-manifold surfaces with multiple connected components, allowing efficient direct manipulation of large, highly detailed meshes, unprocessed raw scanner output, and even point clouds with no connectivity information.
A large phonemic time-delay neural network technique for all Mandarin consonants recognition

A large phonemic time-delay neural network (LPTDNN) technique has been developed to recognize all Mandarin consonants of the entire set of Chinese syllables. By modular construction, six component networks, viz. (b,d,g,ot), (l,m,n,r,ot), (j,z,zh,ot), (c,q,ch,ot), (f,s,x,h,sh,ot) and (p,t,k,ot), are formed to represent all 21 Mandarin consonants (where ot=others). The technique was thoroughly tested with 8 sets of 1157 isolated Hanyu Pinyin syllables, with 6 sets used for training and 2 sets used for testing. The overall result shows a high recognition performance with 99.2% for the inside test (i.e. testing on the datasets which were used entirely in training) and 92.7% for the outside test (i.e. testing on the remaining datasets which were never used in training). <<ETX>>
Cleaning validation, practical considerations.

The EHEDG subgroup Cleaning Validation is working on a new guideline which will be ready in 2014. Cleaning and/or disinfection validation is defined as ‘obtain ing documented evidence that cleaning and/or disinfection processes are consistently effective at reaching a predefined level of hygiene, if properly implemented on equipment and production environment and used as intended’. According to ISO 22000, verification is the ‘confirma tion through the provision of objective evidence that specified requirements have been fulfilled’. The guideline has as goal to provide a complete validation approach suitable for Equipment manufacturers, cleaning products and cleaning equipment manufacturers, and all food producers. Cleaning validation is a documented process that shows evidences to demonstrate that the cleaning methods which have been found applicable and acceptable for a process/ product, achieve consistently the required levels of cleanliness. The objective of the cleaning validation is to demonstrate the effectiveness of the cleaning procedures in the removal of product residues, degraded products, preservatives, allergens, and/or cleaning / disinfecting / cross contamination / enzymatic agents that can post a risk to the consumer. It is a fact that over 80 % of existing cleanings executed on a daily base in the food industry are not validated and poorly documented, and can be one of the root causes of food safety incidents. Validation will require an understanding of all elements involved in the cleaning result such as the design and development for an effective program, the principles and calculations of residue limits for a variety of residue types, routes of administration, and dosage types the selection of available analytical methods, along with appropriate levels of analytical method validation, the selection of sampling methods and sampling sites, along with proper selection of blanks and controls the appropriate strategies and documentation for sampling recovery studies, the presence of a cleaning validation master plan and/or policy components, the appropriate documentation and reports, the tools used for monitoring, verification and revalidation.
Editorial: Digital Transformation & Digital Business Strategy in Electronic Commerce - The Role of Organizational Capabilities

Ayesha Nadeem Babak Abedin Narciso Cerpa Eng Chew Editorial: Digital Transformation & Digital Business Strategy in Electronic Commerce The Role of Organizational Capabilities Journal of Theoretical and Applied Electronic Commerce Research ISSN 0718–1876 Electronic Version VOL 13 / ISSUE 2 / MAY 2018 / I-VIII © 2018 Universidad de Talca Chile This paper is available online at www.jtaer. com DOI: 10.4067/S0718-18762018000200101
Introduction on Bigdata and Hadoop

Definition of big data means simply it is the collection of large amount of data which is in any type of data manner, Big Data describes a new techniques to capture, store, distribute and manage large data sets according to its parameters like volume, velocity and variety and using this parameter big data manage any type of data like structured, semi structured or it may be unstructured. Hadoop is the general open source implementation tool which is designed for deep study and revolution of very large data sets. Hadoop enables us to explore complex data, using custom demand. It is the system which allows unstructured data to be in structured format and it will distribute among number of terminals for sharing purposes. Hadoop has its own file system through this we can replicate data to its multiple terminals, if one terminals goes down then from others terminals we can access piece of information.
Modeling, analysis, and simulation of nonfrequency-selective mobile radio channels with asymmetrical Doppler power spectral density shapes

What terrestrial and satellite land mobile radio channels have in common is that they are usually assumed to be nonfrequency selective. For such fading channels, a highly flexible analytical model is presented. Our model takes into account short-term fading with superimposed long-term lognormal variations of the local mean value of the received signal. It is assumed that the introduced complex stochastical process, which is used for modeling the short-term fading behavior, has a Doppler power spectral density with an asymmetrical shape. Closed solutions are presented showing the influence of asymmetrical Doppler power spectral density shapes on the statistical properties of the proposed channel model. A numerical optimization procedure is shown for the optimization of the model parameters in order to fit the statistics of the analytical model [amplitude probability density function (PDF), level-crossing rate (LCR), and average duration of fades (ADF)] to measurement results of an equivalent land mobile satellite channel for light and heavy shadowing environments. Finally, from the analytical model, an efficient simulation model is derived that enables the simulation of such realistic land mobile satellite channel scenarios on a digital computer.