Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Patulous Kalman Filter Algorithm in UWB Location

With the development of the wireless communication technology, the new wireless network technology has been widely used. Locating technique based on wireless network especially UWB wireless location technique has a cheerful prospect. This paper applies Patulous Kalman Filter algorithm to UWB location technology based on RSSI algorithm, and brings forward the simulation of the performance of RSSI algorithm and Patulous Kalman Filter algorithm. Simulation results demonstrate that Patulous Kalman Filter algorithm can reduce locating error, and increase locating precision.
News Video Text Area Positioning Based on an Improved Trajkovic Corner Detector

Comparing to other videos, text in news video contains more semantic information which are mostly descript the news event. So, the extraction of text plays an important role for obtaining the high-level semantic information from the video. In this paper, the improved Trajkovic corner detection algorithm has been studied based on the characteristics of text in news video. According to the characteristics of text, the adaptive thresholding method of scale in combination with standard deviation (SCSD) can be applied to determine the corners, which aims to accurately obtain the text-related corners. The breadth-first clustering algorithm was also been used to distinguish and plan the detected corners which are in same range to determine the text area in video frame. The accuracy of recognition and semantic exploration of the extracted text can be used to quickly understand news video that can effectively improve the efficiency to summarize the news videos. The experimental results show this method with high pertinence which can better achieve the targets. The detection accuracy of text-related corners can reach to 82.1% which is higher than other methods. Under the same experimental condition, recall ratio and precision ratio of the text area are also improved dramatically.
Test-Driven Development of Embedded Software

Software in embedded systems plays an essential role. Principles of Test-Driven Development can be applied to increase quality of source code. TDD promotes the creation of code driven by automated tests. TDD exists for many years in general software development. This paper focuses on the migration of TDD to embedded system development. However, embedded systems do not lend themselves towards test automation. There are a number of obstacles when applying TDD on embedded systems. Amongst others is the strong hardware dependency of software, or even hardware that is missing at software development launch time. The paper shows that programming to an interface, instead of to a concrete class itself, isolates the software from the hardware. Then virtual drivers, which mock the behavior of real hardware drivers, can be implemented to test business logic behavior. Another option is to include a test wrapper on the embedded system. Based on existing measurements of advantages of TDD in general software development, the paper experiences comparable when applying the TDD methodology to embedded system design. Not only decreases the number of software bugs, furthermore the project's life cycle shortens.
Convolutional neural networks and local binary patterns for hyperspectral image classification

ABSTRACT Convolutional neural networks (CNNs) have strong feature extraction capability, which have been used to extract features from the hyperspectral image. Local binary pattern (LBP) is a simple but powerful descriptor for spatial features, which can lessen the workload of CNNs and improve the classification accuracy. In order to make full use of the feature extraction capability of CNNs and the discrimination of LBP features, a novel classification method combining dual-channel CNNs and LBP is proposed. Specifically, a one-dimensional CNN (1D-CNN) is adopted to process original hyperspectral data to extract hierarchical spectral features and another same 1D-CNN is applied to process LBP features to further extract spatial features. Then, the concatenation of two fully connected layers from the two CNNs, which fused features, is fed into a softmax classifier to complete the classification. The experimental results demonstrate that the proposed method can provide 98.52%, 99.54% and 99.54% classification accuracy on the Indian Pines, University of Pavia and Salinas data, respectively. And the proposed method can also obtain good performance even with limited training samples.
Pengenalan Wajah Menggunakan Metode Elastic Bunch Graph Matching

Pada saat ini, sistem pengenalan wajah sudah banyak digunakan di berbagai aplikasi dan juga metode yang digunakan. Namun terdapat beberapa permasalahan eksternal yang biasa terjadi dalam proses pengenalan wajah yaitu Pose, Illumination, and Expression (PIE). Permasalahan tersebut menyebabkan citra wajah orang yang sama akan dikenali berbeda oleh sistem. Metode Elastic Bunch Graph Matching dapat mengatasi permasalahan tersebut karena proses pengenalan wajah menggunakan titik yang diambil secara manual. Oleh sebab itu pada Tugas Akhir ini membahas pengenalan wajah menggunakan metode Elastic Bunch Graph Matching . Pada metode ini wajah direpresentasikan sebagai graph yang dibentuk dari titik titik fitur yang dibuat secara manual. Setelah mengetahui titik-titik fitur pada wajah, dilakukan perhitungan untuk mendapatkan nilai Jet yang dilanjutkan dengan pembentukan Face Bunch Graph untuk proses pencocokan pada Elastic Bunch Graph Matching . Hasil penelitian menunjukkan bahwa metode ini dapat diterapkan pada pengenalan wajah dengan akurasi 91.67%. Dan dapat mengatasi permalsahan Pose, Illumination, and Expression (PIE) dengan akurasi 70%.
Multivariate Time-series Analysis of Variable Objects in the Gaia Mission

In astronomy, we are witnessing an enormous increase in the number of source detections, precision, and diversity of measurements. Additionally, multi-epoch data is becoming the norm, making time-series analyses an important aspect of current astronomy. The Gaia mission is an outstanding example of a multi-epoch survey that provides measurements in a large diversity of domains, with its broad-band photometry; spectrophotometry in blue and red (used to derive astrophysical parameters); spectroscopy (employed to infer radial velocities, v sin ( i ) , and other astrophysical parameters); and its extremely precise astrometry. Most of all that information is provided for sources covering the entire sky. Here, we present several properties related to the Gaia time series, such as the time sampling; the different types of measurements; the Gaia G, GBP and GRP-band photometry; and Gaia-inspired studies using the CORrelation-RAdial-VELocities data to assess the potential of the information on the radial velocity, the FWHM, and the contrast of the cross-correlation function. We also present techniques (which are used or are under development) that optimize the extraction of astrophysical information from the different instruments of Gaia, such as the principal component analysis and the multi-response regression. The detailed understanding of the behavior of the observed phenomena in the various measurement domains can lead to richer and more precise characterization of the Gaia data, including the definition of more informative attributes that serve as input to (our) machine-learning algorithms.
Emergence of power laws in noncritical neuronal systems

Experimental and computational studies provide compelling evidence that neuronal systems are characterized by power-law distributions of neuronal avalanche sizes. This fact is interpreted as an indication that these systems are operating near criticality, and, in turn, typical properties of critical dynamical processes, such as optimal information transmission and stability, are attributed to neuronal systems. The purpose of this Rapid Communication is to show that the presence of power-law distributions for the size of neuronal avalanches is not a sufficient condition for the system to operate near criticality. Specifically, we consider a simplistic model of neuronal dynamics on networks and show that the degree distribution of the underlying neuronal network may trigger power-law distributions for neuronal avalanches even when the system is not in its critical regime. To certify and explain our findings we develop an analytical approach based on percolation theory and branching processes techniques.
SQL Injection and Proposed Methods: Comparison of tools and efficiency for preventing SQL attacks

SQL Injection continues to be the topmost security risk in the world according to Open Web Application Security Project (OWASP) [1] top 10 vulnerability list of 2013. The ease of exploitability and severe impact are what that lands this attack at top spot. As the countermeasures are made more sophisticated, injection also continues to evolve itself, thus thwarting the attempt to completely tackle this attack. The vulnerable data is a source of worry for government as well as financial institution. In this digital age, the consequence of an unauthorized access to a database could vary from lack of confidentiality of clients to a destabilized nation embroiled in war. In this paper, a detailed survey of types of SQL Injection and proposed methods and theories is been presented along with tools and their efficiency in intercepting and thus preventing SQL attacks.
Towards Classifying and Selecting Appropriate Security Visualization Techniques

Visualization of network security events has become an important method for detecting, responding to, and resolving security incidents. While there are many security visualization tools and techniques available, each one may require a different run-time environment and data input, making it difficult for a network security analyst to try them all (or a significant subset) and select those that work best for a specific incident or purpose. This thesis analyzes three common classes of network attacks that security analysts encounter. Relevant variables which help understand and resolve an incident are identified in each specific class of attack. We then survey a large set of network security visualization techniques and use a task-based methodology to assess the usability, insight gained and overall usefulness of visualization tools for specific classes of attacks. We also recommend the most appropriate techniques for visualizing each attack and suggest other features that could help provide more insight.
Multiscale simulation of frequency dependent line models and network equivalents

A analise de sistemas eletricos engloba fenomenos com diferentes constantes de tempo, o que acarreta na utilizacao de diversas ferramentas de simulacao. Como exemplo, transitorios rapidos envolvendo surtos de manobra demandam passos de integracao na ordem de microssegundos enquanto para transitorios lentos, advindos da troca de energia entre geradores, adotam-se passos de integracao de milissegundos. O presente trabalho investiga a utilizacao de modelos baseados em matrizes de admitância variantes na frequencia para representacao de linhas de transmissao aereas, cabos submarinos e equivalentes de rede em coordenadas de fase para o desenvolvimento de algoritmos para simulacao multiescala. Ao inves da utilizacao do Metodo das Caracteristicas, a matriz de admitância nodal e duas decomposicoes alternativas sao consideradas de modo a contornar a limitacao do passo de integracao em funcao do tempo de trafego de linhas, a saber: o Folded Line Equivalent e a Decomposicao Idempotente. O conceito de Latencia sera tambem investigado de modo a prover uma realizacao mais eficiente de modelos variantes na frequencia. As formulacoes desenvolvidas neste trabalho encontram aplicacao imediata em programas para simulacao de transitorios eletromagneticos, tais como PSCAD, EMTP-RV e ATP dado que e mantida a representacao atraves dos equivalentes de Norton. Por meio de fasores dinâmicos, torna-se viavel a representacao de fenomenos eletromagneticos e eletromecânicos com o mesmo modelo computacional. Casos teste sao empregados na avaliacao do desempenho e precisao das formulacoes propostas. O Metodo das Caracteristicas e a transformada numerica de Laplace sao utilizados para fins de comparacao. Com reduzido esforco computacional, resultados com excelente precisao sao obtidos sem a presenca de oscilacoes numericas ou descontinuidades nas formas de onda.
Diferenciación BIRADS I vs II a través de Espectroscopía de Inducción Magnética: Potencial Método Innovador para la Detección Oportuna de Neoplasias en Mama

Las limitaciones actuales en el tratamiento del Cancer de Mama (CaMa) estan asociadas al diagnostico tardio de la enfermedad. Actualmente no existe una tecnologia portatil y no invasiva que permita el diagnostico de neoplasias en estadios tempranos en la glandula mamaria. La bioimpedancia electrica de tejido neoplasico es diferenciable con respecto a tejido normal. Hemos propuesto realizar mediciones de bioimpedancia a traves de Espectroscopia de Induccion Magnetica (EIM) como una alternativa no invasiva para detectar neoplasias en glandula mamaria. Objetivo. -Explorar la factibilidad de detectar neoplasias en glandula mamaria a traves de EIM y determinar si dicha tecnica permite diferenciar hallazgos mamograficos BIRADS I vs II. Material y Metodos. -Mujeres voluntarias con hallazgos mamograficos BIRADS I y II fueron monitoreadas con un espectrometro inductivo portatil experimental que permite evaluar la influencia de EIM en tejido de mama en el ancho de banda de 0.001 a 100 MHz; se compararon mediciones de EIM, asi como la temperatura corporal y variables antroprometricas. Resultados. -Pequenas diferencias en el corrimiento de fase inductivo a frecuencias inferiores a 100 KHz son evidentes entre grupos BIRADS. Un analisis de comparacion de valores promedio del corrimiento de fase inductivo a cada frecuencia, a traves de la prueba t-student para muestras independientes, mostro diferencias estadisticamente significativas solo a la frecuencia de 1.81 KHz. Discusion y Conclusion. -Mediciones de EIM en tejido de mama en el rango de dipersion dielectrica inferior a 100KHz muestran la factibilidad tecnica de diferenciar hallazgos mastograficos BIRADS I y II. Posibles efectos confusores asociados a la temperatura tisular y artefactos de instrumentacion indican que se requieren amplios estudios de sensibilidad y especificidad que permitan constatar los resultados hasta el momento observados
Crop Type Identification and Mapping Using Machine Learning Algorithms and Sentinel-2 Time Series Data

In this paper, the random forests method and the support vector machine in machine learning are explored and compared to the traditional statistical-based maximum likelihood method with 126 features from Sentinel-2A images. The spectral reflectance of 12 bands, 96 texture parameters, 7 vegetation indices, and 11 phenological parameters are successfully extracted from Sentinel-2A images in 2017. The classification result shows that the optimal combination of 13 features yields overall accuracies of traditional classification and machine learning classification of 88.96% and 98%, respectively. Short-wave infrared information shows a significant effect on distinguishing rice, corn, and soybean. The water vapor band plays a significant role in distinguishing between corn and rice. In the multiclassification problem, the machine learning methods have robustness with the identification accuracy of greater than 95% for each crop type, whereas the traditional classification result shows imbalanced accuracies for different crops.
Efecto de incluir Caesalpinia spinosa en dietas crecimiento y acabado sobre parámetros productivos de pollos de carne

El incremento de la demanda de proteína animal y los altos costos de alimentación para los animales hacen que se busque alternativas de nuevos insumos en la ración animal. El objetivo fue evaluar la inclusión de Caesalpinia spinosa en dietas crecimiento y acabado sobre parámetros productivos de pollos de carne. Se utilizaron 135 pollos Cobb-500 de 15 días de edad, con peso promedio 0.435±0.01 kg. Se evaluó parámetros productivos semanalmente y al final del experimento el mérito económico. Para el análisis de datos, se empleó el DCA con 3 tratamientos [(0 fue el control), 3 y 6% de harina de C. spinosa (HCs)] y 3 repeticiones cada uno. Cuando se detectó un efecto significativo del tratamiento (p<0.05), se analizaron las diferencias entre medias con Tukey en el software Statistix 8. Se encontró diferencias significativas (p<0.05) en los parámetros productivos en todas las semanas; el tratamiento control con el 3% de HCs fueron superiores (p<0.05) respecto al 6% de HCs para peso semanal, incremento de peso, consumo de alimento en la última semana. El tratamiento de 3% de HCs presento hasta S/. 0.36 más que el grupo control y en el 6% de HCs presentó pérdida de S/. 1.40 respeto al control. Se concluye que los parámetros productivos no se ven afectados al incluir hasta 3% de HCs en la ración; además, se observó el mejor mérito económico respecto a los otros grupos.
THE STUDY OF OFF-CENTER RECEPTIVE FIELDS AND THE APPLICATION FOR INFRARED IMAGE

The receptive field (RF) theory and the lateral inhibition (LI) mechanism are of the same view.According to the eye′s microsaccade phenomena supported by the medicine field, the proposed opinion states that image spot of object will project the On center RF and off center RF alternatively in the split seconds, so a new vision mechanism′s assume based on this opinion is proposed; this paper discussed the property of Off center RF in detail, and put forward a mathematic model that has a advantage to carry out the edge detecting algorithm, especially to the military IR image, this algorithm posses the characteristics of speeding and feature magnify.
Retrofitting Privacy into Operating Systems

The changing technology and adversarial models often render existing privacy defenses obsolete, or lead to the emergence of new privacy-sensitive computer resources. This necessitates the security community to develop novel defenses that address contemporary privacy threats. The operating system is a natural platform to design these novel privacy defenses on, as it allows for enforcing correct, strong security properties on applications, and scales to the entire user space. However, developing and deploying a secure operating system from scratch is impractical. In this thesis, we instead argue that extending existing operating systems with novel privacy defenses is the preferable approach to addressing emerging privacy needs, while also sidestepping issues of cost and usability. We show that such an approach is both feasible and effective. To support this claim, we examine four distinct privacy scenarios: (1) keeping privacy-sensitive data produced during short-lived program execution sessions confidential, (2) securely deleting sensitive data persisted to modern storage media for long-term use, (3) hiding the existence of encrypted sensitive data on disk, and finally, (4) providing a user-driven access control model for non-traditional privacy-sensitive resources on a computer. We discuss the contemporary privacy threats pertaining to each scenario, and present four privacy-enhancing techniques to address these issues. We demonstrate that our solutions can be retrofitted into existing operating systems to provide general, application-transparent, and usable defenses.
About the Web Site

Background: The role of fungi in the pathogenesis of respiratory illness has received scant attention in the medical literature. The parallel increases in allergic diseases and informal settlements have necessitated an evaluation of a possible link between the two factors, as mould growth is facilitated by the conditions present in these areas. Objective: This review evaluated the association of airborne mould prevalence in an informal settlement and its relation to childhood respiratory health. Method: A prospective study of an informal settlement, Malakazi near Durban, South Africa was conducted. Factors enhancing mould growth, such as temperature and humidity, were evaluated. Homes were categorised as either study homes or controls, based on building structure, ventilation and occupant behaviours. Mould levels and species were studied both indoors and outdoors. Respiratory health was determined using standardised questionnaires, lung function tests and allergen sensitisation. Result: High fungal loads were seen throughout the informal settlement. Higher indoor to outdoor mould spore count ratios were seen in the study homes as compared to controls. Aspergillus (27%), Penicillium (26%) and Wallemia (23%) were the moulds commonly identified. Respiratory illness as determined by a questionnaire, lung function tests and bronchial reversibility was associated with increased mould growth in study homes. Temperature and humidity changes amplified indoor mould growth in study homes. Mould sensitisation was common in both groups studied. Conclusion: Informal settlements provide conditions to support mould growth. Increased mould growth impacts negatively on childhood respiratory health. Minimum standards for building construction and mould counts to identify 27 Urban Health and Development Bulletin Urban Health and Development Bulletin pathogenic levels are needed to improve health. Further studies are essential to answer these questions.
Putting distributed representations into context

ABSTRACT The merits of distributed representations are widely discussed but, I believe, largely misunderstood. My purpose in this paper is to put the issue of distributed representations into context. I will argue that the term distributed is only meaningful with respect to that which is being represented and that degree to which a localist or a distributed code is more desirable depends on the goals of the computation to be performed. I will also argue that localist codes play an essential role in symbolic neural computation.
Design of Augmented Nonlinear PD Controller of Delta/Par4-Like Robot

This work presents the design of two control schemes for a Delta/Par4-like parallel robot: augmented PD (APD) controller and augmented nonlinear PD (ANPD) controller. The stability of parallel robot based on nonlinear PD controller has been analyzed and proved based on Lyapunov method. A comparison study between APD and ANPD controllers has been made in terms of performance and accuracy improvement of trajectory tracking. Also, another comparison study has been presented between augmented nonlinear PD (ANPD) controller and nonaugmented nonlinear PD (NANPD) controller in order to show the enhancement of introducing the augmented structure on dynamic performance and trajectory tracking accuracy. The effectiveness of augmented PD controllers (APD and ANPD) and nonaugmented nonlinear PD (NANPD) controller for the considered parallel robot are verified via simulation within the MATLAB environment.