Miracle-dz/UniFAR-FTUs huggingface dataset:
Degree-Driven Design of Geometric Algorithms for Point Location, Proximity, and Volume Calculation

DAVID L. MILLMAN: Degree-Driven Design of Geometric Algorithms for Point Location, Proximity, and Volume Calculation (Under the direction of Jack Snoeyink) Correct implementation of published geometric algorithms is surprisingly difficult. Geometric algorithms are often designed for Real-RAM, a computational model that provides arbitrary precision arithmetic operations at unit cost. Actual commodity hardware provides only finite precision and may result in arithmetic errors. While the errors may seem small, if ignored, they may cause incorrect branching, which may cause an implementation to reach an undefined state, produce erroneous output, or crash. In 1999 Liotta, Preparata and Tamassia proposed that in addition to considering the resources of time and space, an algorithm designer should also consider the arithmetic precision necessary to guarantee a correct implementation. They called this design technique degree-driven algorithm design. Designers who consider the time, space, and precision for a problem up-front arrive at new solutions, gain further insight, and find simpler representations. In this thesis, I show that degree-driven design supports the development of new and robust geometric algorithms. I demonstrate this claim via several new algorithms. For n point sites on a U × U grid I consider three problems. First, I show how to compute the nearest neighbor transform in O(U) expected time, O(U) space, and double precision. Second, I show how to create a data structure in O(n logUn) expected time, O(n) expected space, and triple precision that supports O(log n) time and double precision post-office queries. Third, I show how to compute the Gabriel graph in O(n) time, O(n) space and double precision. For computing volumes of CSG models, I describe a framework that uses a minimal set of predicates that use at most five-fold precision.
Automatic Text Summarization for Bengali Language Including Grammatical Analysis

: Text summarization is a process of summarize any text or document. There are many summarization tools for English language. There are also a few works for automated Bengali text or document summarization. The tools are seemed not much appropriate from application point of view. Summarization is categorized in two ways: extractive and abstractive approach. Most of the summarizer methods for Bengali text summarization are extractive. Those proposed methods can‘t extract whole theme of a text document. Reader can be satisfied about summary if it gives full important information of input document. Our proposed method introduces an enhanced summarization method that can improved the quality of outputs. The proposed method is modeled combining a set mathematical rules and Bengali grammatical rules. This method also solves many problems of extractive summarizer and it also introduces the path of abstractive summarization methods. Although the method has been developed for Bengali language, it is a generic and platform independent approach and can flexible be extended for other languages.
Utilizing HRV-Derived Respiration Measures for Driver Drowsiness Detection

This study aims to utilize heart rate variability (HRV) signals obtained with a wearable sensor for driver drowsiness detection. To this end, we investigated respiration characteristics derived from HRV signals based on the known fact that respiratory activity can be estimated from the high frequency (HF) band of HRV signals. For drowsiness detection, many earlier works commonly used dominant respiration (DR) characteristics. However, in some situations where emphasized power in a power spectrum of HRV occurs at multi sub-frequency, the DR measures may possibly fail to capture overall respiration characteristics. To handle this problem, we propose two spectral indices, the weighted mean (WM) and the weighted standard deviation (WSD) of the HF band in the power spectrum. These indices are used to properly capture the overall shape of the respiratory activity shown through the HF band of the HRV power spectrum as an alternative to the DR measures. For experiments, we collected HRV data with an electrocardiogram device worn on the body under a virtual driving environment. The proposed indices somewhat clearly showed the tendency that respiratory frequency decreases and respiration regularity increases in drowsy states of all subjects, while existing DR measures hardly showed this. In addition, when the proposed indices are used alone or together with conventional HRV-related measures as input features for classification models, they showed the best performance in distinguishing drowsiness from wakefulness.
Analysis and Control of Neutral-Point Deviation in Three-Level NPC Converter Under Unbalanced Three-Phase AC Grid

This paper presents a neutral-point deviation and ripple compensating control method applied to a three-level neutral-point-clamped (NPC) converter. The neutral-point deviation and its harmonic components are analyzed with a focus on the average current flowing through the neutral-point of the dc link. This paper also proposes a control scheme compensating for the neutral-point deviation and dominant harmonic components under generalized unbalanced grid operating conditions. The positive and negative sequence components of the switching functions and ac input currents are employed to accurately explain the behavior of three-level NPC converter. Simulation and experimental results are presented to verify the validity of the proposed method.
What lies in the shadows? Safe and computation-aware motion planning for autonomous vehicles using intent-aware dynamic shadow regions

One of the challenges of developing autonomous vehicles is planning in an inhabited environment under sensing uncertainty as well as limited perception and computational resources. Besides reasoning about the behaviour of traffic participants that are within the vehicles’ field of view, safe autonomous driving also requires the vehicle to reason about possible traffic participants that might exist beyond its sensing horizon, and to adapt its driving behaviour accordingly. This paper describes an inference and motion planning pipeline that is able to guarantee passive safety (collisions are possible, but the autonomous vehicle will be at rest) with respect to hypothetical hidden agents that have not been observed yet. We also incorporate the vehicle’s reaction time due to sensing and computational delays into the planning process; for example, we show how having a fast reaction time due to the availability of more computational resources leads to more aggressive trajectories, while a car with a larger reaction time will choose more relaxed trajectories that require less attention.
Sensitivity Simulation of Compressed Sensing Based Electronic Warfare Receiver Using Orthogonal Matching Pursuit Algorithm

Abstract : The wideband coverage of the traditional fast Fourier transform (FFT)-based electronic warfare (EW) receiver is limited by the sampling frequency of the analog-to-digital converter (ADC). Compressed sensing (CS) based wideband EW digital receiver has been proposed and under development to process wideband sparse signals with reduced sampling rate. In this study, we calculated the sensitivity of a CS-based EW receiver. Two modulation schemes of CS are used. One is pseudorandom chip (PRC) and the other is non-uniform sampling (NUS) modulation. The orthogonal matching pursuit (OMP) algorithm is used to process CS data. The insufficient nature of the sparcity of the signal adversely affects the signal detection probability for both PRC and NUS modulation schemes. Methods to remedy this problem in both modulation schemes are proposed. The improvement in the signal detection and thus the sensitivity performance of the CS based receiver are presented.
Region-Based Automated Localization of Colonoscopy and Wireless Capsule Endoscopy Polyps

The early detection of polyps could help prevent colorectal cancer. The automated detection of polyps on the colon walls could reduce the number of false negatives that occur due to manual examination errors or polyps being hidden behind folds, and could also help doctors locate polyps from screening tests such as colonoscopy and wireless capsule endoscopy. Losing polyps may result in lesions evolving badly. In this paper, we propose a modified region-based convolutional neural network (R-CNN) by generating masks around polyps detected from still frames. The locations of the polyps in the image are marked, which assists the doctors examining the polyps. The features from the polyp images are extracted using pre-trained Resnet-50 and Resnet-101 models through feature extraction and fine-tuning techniques. Various publicly available polyp datasets are analyzed with various pertained weights. It is interesting to notice that fine-tuning with balloon data (polyp-like natural images) improved the polyp detection rate. The optimum CNN models on colonoscopy datasets including CVC-ColonDB, CVC-PolypHD, and ETIS-Larib produced values (F1 score, F2 score) of (90.73, 91.27), (80.65, 79.11), and (76.43, 78.70) respectively. The best model on the wireless capsule endoscopy dataset gave a performance of (96.67, 96.10). The experimental results indicate the better localization of polyps compared to recent traditional and deep learning methods.
Symmetrical PLL for SISO Impedance Modeling and Enhanced Stability in Weak Grids

This paper proposes a symmetrical phase-locked loop (PLL) that can eliminate the frequency-coupling terms caused by the asymmetric dynamics of conventional PLLs. In the approach, a concept of complex phase angle vector with both real and imaginary phase components is introduced, which enables to control the direct- and quadrature-axis components with symmetrical dynamics. The small-signal impedance model that characterizes the dynamic effect of the symmetrical PLL on the current control loop is also derived, which, differing from the conventional multiple-input multiple-output impedance matrix, is in a single-input single-output (SISO) form based on complex transfer functions. This SISO representation allows for a design-oriented analysis. Moreover, the undesired sub-synchronous oscillation caused by the conventional asymmetrical PLL can be avoided, and the classical SISO impedance shaping can be utilized to cancel the negative resistor behavior caused by PLL; thus can greatly enhance the grid synchronization stability under weak grid conditions. The effectiveness of the theoretical analysis is validated by experimental tests.
Probabilistic Encoding and Decoding of Neural Activity in Rat Primary Visual Cortex

: According to the information processing paradigm in the Cognitive Sciences, one of the nervous system’s most important functions is to encode information about the environment. Understanding the neural code means understanding the relationship between brain states and real events in the outer world. In this work, a probabilistic framework for encoding (i.e., mapping events to neural response) and decoding (i.e., reconstruction of events from neural responses) of neural activity is presented and applied to theoretical and empirical data. Reconstruction is based on a Bayesian ﬁlter method, allowing the propagation of multi modal densities and the use of non-linear stimulus dynamics. The model is tested against empirical data recorded from rat primary visual cortex. Cells in this area are reported to be orientation-selective and their neural response characteristics can be learned by presenting oriented gratings to the rat’s eye. The learned ﬁring model is then used to reconstruct an unknown random walk stimulus from neural activity.
Review of GB electricity distribution system's electricity security of supply, reliability and power quality in meeting UK industrial strategy requirements

The United Kingdom (UK) Government's 2017 Industrial Strategy outlines a bold plan centred on making the UK one of the most competitive places in the world to start or grow a business, building on the UK's strengths, extending excellence into the future, and closing the gap between the UK's best performing companies, industries, places and people, and those viewed as less productive. In the context of delivering this strategy, from power system reliability and power quality points of view, this study explores the various challenges and gaps in the Great Britain (GB) electricity distribution system, which is recognised as among the most complex forms of energy exchange that will become the backbone of the emerging digital economy and Industry 4.0. Additionally, the study also provides recommendations to address the identified challenges.
Zero-Sequence Voltage Elimination for Dual-Fed Common DC-Link Open-End Winding PMSM High-Speed Starter–Generator—Part I: Modulation

In this paper, a dual-fed (DF) common dc-link topology open-end winding permanent magnet synchronous motor for aircraft high-speed starter–generator application is considered. While on one hand the common dc bus configuration significantly simplifies and reduces the costs of the topology, on the other hand it allows the zero-sequence current (ZSC) to flow freely in the system. High-speed machines are characterized by low phase inductance, which implies low zero-sequence impedance. A small time constant of the zero-sequence circuit produces a high-frequency, high-intensity ZSC ripple with the risk of harming the switching devices. This paper presents a novel hybrid space vector pulsewidth modulation that allows to instantaneously eliminate the zero-sequence voltage (ZSV) produced by the two voltage source converters (VSCs) by square wave modulating one of the two VSCs. The non-sinusoidal machine back electromotive force (EMF) has been considered and the effect of the converters’ dead time on the ZSV has been analyzed. The square-wave-modulated VSC uses insulated-gate bipolar transistor (IGBT) devices, whereas the other uses Silicon Carbide (SiC) technology. The proposed topology is tested through both simulations and experiments.
Optimal Operation Scheduling of a Microgrid Incorporating Battery Swapping Stations

This paper proposes optimal operation scheduling of a Microgrid (MG) and battery swapping stations (BSSs) as two independent stakeholders with inherently conflicting objectives. In this regard, a bi-level scheduling framework for optimal decision making of MG and BSSs is presented. Moreover, battery degradation cost is explicitly modeled based on the depth of discharge and the cycle life's intrinsic behavior of batteries. In order to tackle both historical data-based and human-related uncertainties under incomplete information including load demand of MG, photovoltaic generation, wholesale market prices, and swapping requests, a hybrid probabilistic-possibilistic approach considering correlation among uncertainties has been developed. To solve the proposed MG-BSS optimization problem, alternative direction method of multipliers (ADMM) with restart algorithm in a fully decentralized fashion is implemented. The effectiveness of the proposed model is demonstrated on a real-test MG system under different scenarios. Moreover, to compare the computational complexity of the proposed algorithm with the standard ADMM and investigate the scalability of the algorithm, extensive simulations are carried out on different standard test system.
Procedural Method for Fast Table Mountains Modelling in Virtual Environments

Natural terrains created by long-term erosion processes can sometimes have spectacular forms and shapes. The visible form depends often upon internal geological structure and materials. One of the unique terrain artefacts occur in the form of table mountains and can be observed in the Monument Valley (Colorado Plateau, USA). In the following article a procedural method is considered for terrain modelling of structures, geometrically similar to the mesas and buttes hills. This method is not intended to simulate physically inspired erosion processes, but targets directly the generation of eroded forms. The results can be used as assets by artists and designers. The proposed terrain model is based on a height-field representation extended by materials and its hardness information. The starting point of the technique is the Poisson Faulting algorithm that was originally used to obtain fractional Brownian surfaces. In the modification, the step function as the fault line generator was replaced with a circular one. The obtained geometry was used for materials’ classification and the hardness part of the modelled terrain. The final model was achieved by the erosive modification of geometry according to the materials and its hardness data. The results are similar to the structures observed in nature and are achieved within an acceptable time for real-time interactions.
Priorización de despachos en empresas de manufactura usando QFD difuso

Las restricciones de capacidad instalada y de recursos de distribución a las que normalmente se encuentran sometidas las medianas y grandes empresas manufactureras, hacen que surja la necesidad de establecer métodos que sirvan como herramienta para tomar decisiones en cuanto a la priorización de cada una de sus bodegas al momento de abastecerlas. La importancia que el centro de distribución asigna a cada una de estas, se encuentra estrechamente relacionada con las condiciones y particularidades que las caracterizan, pudiendo ser estos factores de tipo cualitativo, cuantitativo o una mezcla entre los dos. Este artículo presenta un método multicriterio QFD difuso el cual, además de utilizar variables cuantitativas para establecer el orden de despacho de producto terminado, tiene en cuenta variables cualitativas, resultado de las opiniones que los expertos pueden ofrecer acerca de las situaciones particulares de cada bodega mediante variables lingüísticas que, finalmente, se cuantifican a través de números difusos triangulares.
A Novel Event-centric Trend Detection Algorithm for Online Social Graph Analysis

Nowadays, the identification of the most popular and important topics discussed over social networks, is became a vital societal concern. For real-time tracking the hot topics, we proposed a novel event-centric trend detection algorithm, which called Ec_TD algorithm to attempt to add event attributes into the structure of the social networks, then, mining the subgraphs induced by specific attributes which using correlation function measures the correlation of event-changing attributes based on the attribute-extended social network structure. Our experiment shows that Ec_TD algorithm is performed significantly better in real-time event detecting and mining the potential relationships between attributes and vertexes. Moreover, we used true big data to test this algorithm which has substantially reduced respond time, and to prove the feasible of the idea.
Intermediate-Scale Full State Quantum Circuit Simulation by Using Lossy Data Compression

References [1] Di, Sheng, and Franck Cappello. "Fast error-bounded lossy HPC data compression with SZ. " 2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 2016. [2] Smelyanskiy, Mikhail, Nicolas PD Sawaya, and Alán Aspuru-Guzik. "qHiPSTER: the quantum high performance software testing environment. " arXiv preprint arXiv:1601.07195 (2016). [3] Dingwen Tao, Sheng Di, Franck Cappello, "A Novel Algorithm for Significantly Improving Lossy Compression of Scientific Data Sets, " in International Parallel and Distributed Processing Symposium (IEEE/ACM IPDPS 2017), Orlando, Florida, 2017. [4] Xin Liang, Sheng Di, Dingwen Tao, Zizhong Chen, Franck Cappello, "Efficient Transformation Scheme for Lossy Data Compression with Point-wise Relative Error Bound", in IEEE CLUSTER 2018.
Percepções práticas sobre a adequação de postos de trabalho de manufatura industrial para a inclusão de pessoas com deficiência

A inclusão de pessoas com deficiência em postos de trabalho de manufatura industrial ainda é bastante complexa, a se considerar as limitações e restrições, tanto da estrutura física quanto das habilidades pessoais. Assim, pesquisas que busquem a experiência dos envolvidos para entender modos de realizar este processo de inclusão tornam-se relevantes. Este artigo apresenta o desenvolvimento de uma pesquisa que visa identificar a percepção de trabalhadores de produção industrial sem deficiência sobre como estão adequados os postos de trabalho industriais para a inclusão de pessoas com deficiência. Para isso, se apoia numa pesquisa exploratória de abordagem qualitativa com aplicação de entrevistas semiestruturadas. A partir dos dados coletados, verificou-se que os trabalhadores entendem que os postos não estão adequados, assim como existem várias dificuldades de incluir estas pessoas. As conclusões auxiliam no entendimento do ponto de vista dos trabalhadores atuais e na prospecção de novos modelos de inclusão.
Cost-Effective Localization Using RSS From Single Wireless Access Point

This paper presents a cost-effective localization method by using received signal strength (RSS) from single wireless access point (AP) that has only three directional antennas. To reduce the requirement for the costly database training process, a three-step parametric model-based approach is proposed. This approach estimates the AP-device angle, distance, and the device location analytically by making use of wireless path-loss models, antenna radiation patterns, and differential RSS. Meanwhile, the differential antenna gain is introduced to eliminate the need for known AP-device distances when estimating the AP-device angle. Furthermore, to circumvent the difficulty in detecting outliers in a single-AP system, this paper further integrates single-AP solutions with dead-reckoning (DR) to obtain more robust solutions. Three types of measurement models (i.e., the device location updates, AP-device angle updates, and AP-device distance updates) are built from the single-AP localization system. Static and dynamic field tests with the new Bluetooth 5 signals indicated the possibility for using RSS from three directional antennas on single AP to obtain a low-cost localization solution and the effectiveness of integrating with DR on mitigating the impact of outliers.
Implementation of Science Practical Work at Faculty of Teacher Training and Educational Science, Universitas Terbuka, Indonesia

Science practical work is an important part of Biology Teacher Education Program (BTEP) curriculum in order to (1) show confirmation and illustration of theory, and (2) develop students’ knowledge and skills to teach successfully in junior and senior high school science laboratory. This paper describes how UT conducts the practical work and how student’s perception toward the implementation of practical work. The paper was based upon the study of the practical work implementation in the first semester of 2011, at an UT-Regional Office (RO). Data were collected by interviewing practical work instructors, distributing a questionnaire to 15 students, and observing practical work activity. The results of the study revealed that (1) UT/RO collaborated with a face to face institution of higher education to facilitate BTEP students doing practical work activity, (2) the students actively participated to accomplish the required tasks of practical works and wrote the report, (3) practical work assessment was based upon students’ performance and practical work report, and (4) students considered that practical work will develop their understanding of scientific concepts, science skills, curiosity, and ability to work carefully and systematically. The conclusion of the study were (1) implementation of practical work tended to be used for confirmation and illustration theory and presentation experience for future teaching, and (2) students well and actively performed the practical work, were capable to write the report, and revealed positive perception toward the practical work.