Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis of Turbo Codes Using CRC and FC with OFDM

— Long Term Evolution (LTE) is an evolving wireless communication standard that requires high data rate, higher bandwidth efficiency and better coverage. So, spectrally efficient techniques like Orthogonal Frequency Division Multiplexing (OFDM), Multiple Input Multiple Output and Error Control Codes with high coding gain are constituents of LTE. OFDM partitions a channel with wide bandwidth into several channels with narrow bandwidth that are flat, in order to mitigate the effects of multipath fading and Inter Symbol Interference (ISI). Error control codes enhance reliable transmission over noisy fading channels. The objective of this work is to increase data rate and reduce bit error rate by combining OFDM and Turbo Codes. Bit error rate is further reduced by applying Cyclic Redundancy Check (CRC) and Flip and Check algorithm (FC) along with turbo codes. In this paper, MAP and MAX Log MAP algorithms have been applied in the turbo decoding process and their performance compared. The proposed algorithm has been implemented and simulated using MATLAB.
Visualisation and Communication of VGI Quality

The flourishing of VGI projects has transformed the average web user into an eager geographic data user and contributor. As it is difficult for the crowd to perceive VGI quality, visualisation can play a critical role in communicating data quality. At the same time, although VGI quality has been a prominent research topic for scientists, quality visualisation has not been exploited to its full potential. Since the crowd encompasses a diverse pool of users, VGI quality visualisation caters for different needs and exhibits variable functionality, operating as an awareness tool for the novice user as well as an exploration tool for the expert user / scientist. The scope of this chapter is to present a framework for VGI quality visualisation that takes into account factors such as methods for quality visualisation of spatial data, the nature of VGI data quality, user profiles and the visualisation environment. In addition, a review of the available methods for data quality visualisation, which have emerged from cartography, is presented, and a number of guidelines for VGI quality visualisation are proposed, taking into account user characteristics.
Real-time Human Detection under Omni-dir ectional Camera based on CNN with Unified Detection and AGMM for Visual Surveillance

In this paper, we propose a new real-time human detection under omni-directional cameras for visual surveillance purpose, based on CNN with unified detection and AGMM. Compared to CNN-based state-of-the-art object detection methods. YOLO model-based object detection method boasts of very fast object detection, but with less accuracy. The proposed method adapts the unified detecting CNN of YOLO model so as to be intensified by the additional foreground contextual information obtained from pre-stage AGMM. Increased computational time incurred by additional AGMM processing is compensated by speed-up gain obtained from utilizing 2-D input data consisting of grey-level image data and foreground context information instead of 3-D color input data. Through various experiments, it is shown that the proposed method performs better with respect to accuracy and more robust to environment changes than YOLO model-based human detection method, but with the similar processing speeds to that of YOLO model-based one. Thus, it can be successfully employed for embedded surveillance application.
Assessing the Impact of Holocaust Education on Adolescents’ Civic Values: Experimental Evidence from Arkansas

Background American adults overwhelmingly agree that the Holocaust should be taught in schools, yet few studies investigate the potential benefits of Holocaust education. Objectives We evaluate the impact of a Holocaust education conference on knowledge of the Holocaust and several civic outcomes, including “upstander” efficacy (willingness to intervene on behalf of others), likelihood of exercising civil disobedience, empathy for the suffering of others, and tolerance of others with different values and lifestyles. Research Design We recruit two cohorts of students from three local high schools and randomize access to the Arkansas Holocaust Education Conference, where students have the chance to hear from a Holocaust survivor and to participate in breakout sessions led by Holocaust experts. Results We find evidence that the conference increased participants’ upstander efficacy, but fail to reject the null hypothesis that the conference would increase participants’ knowledge or other civic attitudes.
A Schematic View of the Application of Big Data Analytics in Healthcare Crime Investigation

Abstract Background and Purpose: One major challenge encountered during crime investigation via automated systems is the inability of conventional data analysis techniques to adequately handle the enormous data that are made available during the investigation. Existing crime investigation frameworks are built on orthodox data analysis techniques which cannot sufficiently manned the unprecedented size and variety of data available today, not to mention the significantly more anticipated data in the near future. This has affected the healthcare industry where data is predominantly multi-structured and is growing at a considerably faster rate. Methods: To address this, a big data analytics model based on deep learning was designed in this research using enterprise application diagrams. Results: This model is intended to be implemented using Apache Hadoop a big data implementation framework. When implemented, the model will create a platform that will handle a phenomenon that is affecting millions of people all over the world. Conclusions: This is the first of its kind to use big data analytics techniques in healthcare crime investigation in Nigeria which provided security intelligence by shortening the time of correlating and deriving evidence from large volume of data for healthcare crime investigation purposes. Finally, this research also enabled the healthcare systems to systematically use big data analytics to identify inefficiencies and best practices that improve care delivery and reduce costs. Keywords: Crime, Hadoop, Deep Learning, Investigation Data Analytics, Health Insurance
Image-laser fusion for 3D modeling in complex environments

One might wonder what can be gained from the image-laser fusion and in which measure such a hybrid system can generate automatically complete and photorealist 3D models of difficult to access and unstructured underground environments. In such environments, special attention must be given to the main issue standing behind the automation of the 3D modeling pipeline whic is represented by the capacity to match reliably image and laser data in GPS-denied and feature-less areas. In addition, time and in-situ access constraints require fast and automatic procedures for in-situ data acquisition, processing and interpretation in order to allow for in situ verification of the 3D scene model completeness. Finally, the currently generated 3D model represents the only available information providing situational awareness based on which autonomous behavior must be build in order to enable the system to act intelligently onthe- fly and explore the environment for ensuring the 3D scene model completeness. This dissertation evaluates the potential of a hybrid image-laser system for generating in-situ complete and photorealist 3D models of challenging environments, without requiring human operator intervention. The presented research focuses on two main aspects: (i) the automation of the 30 modeling pipeline, targeting the automatic data matching in feature-less and GPS-denied areas for in situ world modeling and (ii) the exploitation of the generated 3D models along with visual servoing procedures to provide mobile systems with autonomous site digitization and exploration capabilities. We design a complementary and cooperative image-laser fusion which lead to a 4D mosaicing sensor prototype.
A mixed model for estimating the probabilistic worst case execution time

Probabilistic analysis are considered nowadays an interesting solution for real-time systems as the probability of appearance of worst-case values is small (10−45 per hour of functioning) compared to the accepted probability of failure (10−9 per hour of functioning for the highest safety level in avionics). In order to take into account this information, Burns and Edgar [1] have introduced the notion of probabilistic worst case execution time (pWCET). The pWCET of a program is bounding the probability that the execution time of that program exceeds a given value. A possible method to estimate the pWCET is based on measurements and the associated analysis is called measurement-based probabilistic timing analysis (MBPTA). Such method has been proposed by Cucu-Grosjean et al. [2] and the obtained estimate is sensitive to the observed execution times. To our best knowledge this dependence of MBPTA on the observations is an open problem. Within this paper we propose a first solution based on a mixed model using genetic algorithms.
Hybrids of generative and discriminative methods for machine learning

In machine learning, probabilistic models are described as belonging to one of two categories: generative or discriminative. Generative models are built to understand how samples from a particular category were generated. The category chosen for a new data-point is the category whose model fits the point best. Discriminative models are concerned with defining the boundaries between the categories. The category chosen for a new data-point then depends on which side of the boundary it belongs to. While both methods have different / complementary advantages, they cannot be merged in a straightforward way. The challenge we wish to undertake in this thesis is to find rigorous models blending these two approaches, and to show that it can help find good solutions to various problems. Firstly, we will describe an original hybrid model [50] that allows an elegant blend of generative and discriminative approaches. We will show: • how a hybrid approach can lead to better classification performance when most of the available data is unlabelled, • how to make the optimal trade-off between the generative and discriminative extremes, • and how the amount of labelled data influences the best model, by applying this framework on various data-sets to perform semisupervised classification. Secondly, we will present a hybrid approximation of the belief propagation algorithm [51], that helps optimise a Markov random field of high cardinality. The contributions of this thesis on this issue are two-folded: • a hybrid generative / discriminative method Hybrid BP that significantly reduces the state space of each node in the Markov random field, • and an effective method for learning the parameters which exploits the memory savings provided by Hybrid BP. We will then see how reducing the memory needs and allowing the Markov random field to take higher cardinality was useful.
Detection of Inter-turn Winding Fault in Single-phase Transformers Using a Terminal Measurement Based Modeling Technique

Supervisory Committee Dr. Subhasis Nandi Supervisor (Department of Electrical and Computer Engineering) Dr. A. K. S. Bhat Department Member (Department of Electrical and Computer Engineering) Transformers form a very important part of the power transmissions and distribution network; as they are responsible for the transfer of electrical energy from the power generation sites onto the transmission lines and finally to the distribution stage. Dry-type and oil-filled single-phase transformers, either alone or as a part of three-phase banks, are used extensively in the power distribution network, ultimately providing power to the domestic consumers. Any faults in the single-phase transformers leading to power outages or catastrophic power systems failures cause huge loss of capital, property and in some cases even human casualties. Gradual deterioration of the electrical winding insulation ultimately leads to inter-turn winding short circuit faults; which account for a significant proportion of all transformer failures. Incipient stages of inter-turn winding faults have negligible impact on the terminal voltages and currents of transformers; thus these faults often go undetected by the traditional differential relay based protection mechanisms. By the time, the faults manifest themselves into severe winding short-circuit faults consequently forcing the differential relays to operate for tripping the circuit breakers; a significant part of the transformer windings and core may get extensively damaged. Over the years, various techniques have been developed for detecting and studying inter-turn winding faults; however their practical implementation involves quite a few challenges such as high cost, lack of reliability, low
Bipolar Stimulation Of Cardiac Tissue: A Bidomain Model

One of the fundamental problems in cardiac electrophysiology that has not yet been completely elucidated is the response of cardiac tissue to externally applied electric currents. A limited number of theoretical and experimental techniques has been used to study the electric behavior of cardiac tissue due to stimulating currents, and they have demonstrated that the tissue anisotropy plays an important role in both the genesis and propagation of the activation wavefront and the resulting potential distributions. In this work we study the electric behavior of a slice of cardiac tissue subjected to bipolar stimulationusing the bidomain model.
Global features with identical twins biometric identification system

Studies in pattern recognition domain currently revolve around twin’s biometric identification. The twins’ biometric Identification system may lead to the discovery of a distinguishing pattern of a biometric of an individual. A significant improvement can also be seen in the Unimodal biometric identification; it allows accurate and reliable identification of identical twins with good performance of certain traits. However, since the similarity level is very high, Identical twins’ identification is much more difficult when compared to that of non-twins. Hence, the use of more than one biometric trait with global features is proposed. Further, pattern recognition requires the extraction and selection of meaningful features, which leads to the key issue in the identification of twin handwriting-fingerprint, that is, the question of how to acquire features from many writing and styles twin handwriting-fingerprint to enable the reflection of the right person between twins. This study thus proposes the global with Aspect United Moment Invariant for global feature extractions with the application of identical twin multi-biometric identification with Inter-class and Intra-class.
Extracting an Etymological Database from Wiktionary

Electronic lexical resources almost never contain etymological information. The availability of such information, if properly formalised, could open up the possibility of developing automatic tools targeted towards historical and comparative linguistics, as well as significantly improving the automatic processing of ancient languages. We describe here the process we implemented for extracting etymological data from the etymological notices found in Wiktionary. We have produced a multilingual database of nearly one million lexemes and a database of more than half a million etymological relations between lexemes.
Embedded computing solutions used in a multicamera corneal topographer

A novel multi-camera corneal topographer arrangement is proposed in the paper. The arrangement uses a novel colour-coded measurement pattern that facilitates correspondence detection. The topographer relies on a novel surface reconstruction method. It takes images shot by up to four synchronized cameras as inputs. The surface reconstruction is accomplished by the joint solution of multiple partial differential equations (PDE’s). Each of these PDE’s describes the phenomenon of light reflection on specular corneal surface with respect to the common measurement pattern on one hand, and a particular camera in the multi-camera arrangement on the other. To some extent, even the present version of the topographer arrangement makes use of embedded computing concepts; however, their presence and importance in the design are expected to increase with the newer versions of the measurement device.
The Effects of Exposure to Air Pollution on Subjective Well-Being in China

This paper studies the impact of six main air pollutants on three key dimensions of subjective well-being (SWB) â€“ life satisfaction, hedonic happiness and mental health. We match a nationally representative survey in China with local air quality and rich weather conditions according to the exact date and county of each interview. By making use of variations in exposures to air pollution across similar respondents living in the same county, we find that PM2.5 reduces hedonic happiness and increases the rate of depressive symptoms, but does not affect life satisfaction. Our results show that the benefits of reducing air pollution would be higher if the hidden costs of air pollution on SWB in China are taken into account.
Artifacts reduction in low-contrast neurological imaging with C-arm system

C-arm cone-beam CT (CBCT) is adopted rapidly for imaging-guidance in interventional and surgical procedures. However, measured CBCT data are truncated often due to the limited detector size especially in the presence of additional interventional devices outside the imaging field of view (FOV). In our previous work, it has been demonstrated that a constrained optimization-based reconstruction with an additional data-derivative fidelity term can effectively suppress the truncation artifacts. In this work, in attempt to evaluate the optimization-based reconstruction, two task-relevant metrics, are proposed for characterization of the recovery of the low-contrast objects and the reduction of streak artifacts. Results demonstrate that the optimization program and the associated CP algorithms can significantly reduce streak artifacts, leading to improved visualization of lowcontrast structures in the reconstruction relative to clinical FDK reconstruction.
Why Nominal-Typing Matters in OOP

The statements `inheritance is not subtyping' and `mainstream OO languages unnecessarily place restrictions over inheritance' have rippled as mantras through the PL research community for years. Many mainstream OO developers and OO language designers however do not accept these statements. In \emph{nominally-typed} OO languages that these developers and language designers are dearly familiar with, inheritance simply is subtyping; and they believe OO type inheritance is an inherently nominal notion not a structural one. Nominally-typed OO languages are among the most used programming languages today. However, the value of nominal typing to mainstream OO developers, as a means for designing robust OO software, seems to be in wait for full appreciation among PL researchers--thereby perpetuating an unnecessary schism between many OO developers and language designers and many OO PL researchers, with each side discounting, if not even disregarding, the views of the other. In this essay we strengthen earlier efforts to demonstrate the semantic value of nominal typing by presenting a technical comparison between nominal OO type systems and structural OO type systems. Recently, a domain-theoretic model of nominally-typed OOP was compared to well-known models of structurally-typed OOP. Combined, these comparisons provide a clear and deep account for the relation between nominal and structural OO type systems that has not been presented before, and they help demonstrate the key value of nominal typing and nominal subtyping to OO developers and language designers. We believe a clearer understanding of the key semantic advantage of pure nominal OO typing over pure structural OO typing can help remedy the existing schism. We believe future foundational OO PL research, to further its relevance to mainstream OOP, should be based less on structural models of OOP and more on nominal ones instead.
Evaluation of the usability of a new ITG instrument to measure hard and soft governance maturity

IT governance (ITG) has stayed a challenging matter for years. Research suggests the existence of a gap between theoretical frameworks and practice. Although current ITG research is largely focused on hard governance (structure, processes), soft governance (behavior, collaboration) is equally important and might be crucial to close the gap. The goal of this study is to evaluate the usability of a new ITG maturity instrument that covers hard and soft ITG in detail. We conducted ten case studies and evaluated the instrument positively on usability; but feedback also revealed that the assessment questions needed improvements. We demonstrate that combining the instrument with structured interviews results in an enhanced and usable instrument to determine an organization’s current level of hard and soft ITG. We conclude that this new instrument demonstrates a way to reduce the mismatch between ITG maturity theory and practice.
smart vacuum cleaner

An intelligent vacuum cleaner, comprising a body and a charging base, with the body ceiling backfilled received signal lamp, drive means, contact piece, and a controller, and refill signal receiving dome lamp disposed opposite and spaced, the controller sends an instruction to the drive means for rotating or driving body along a linear movement, the charging station is provided with a first signal-emitting lamp, a second signal lamp, lamp holder and a charging shrapnel, transmitting a first signal and a second signal light emitting lamp fixed to the lamp holder the PCB, a first signal and a second signal-emitting lamp emitting light parallel to and spaced from, the first signal and the second signal transmitting lamp emitting light respectively emit different coded signals after the lamp holder is formed with the first signal and the second shield signal strip, and a ceiling light receiving refill signals are configured to receive the first signal band or / and a second signal band, when the body is moved to the charging base charging contact piece and the contact charging shrapnel to achieve ontology automatic charging, high degree of intelligence.
The design of delay-dependent wide-area PSS for interconnected power system with prescribed degree of stability σ

The usage of remote signals obtained from a wide-area measurement system (WAMS) introduces time delays to a wide-area PSS, which would degrade system damping and even cause instability. This paper presents a novel method for designing a delay-dependent wide-area PSS to improve the angular stability of multi-machine interconnected power system with delay margin and prescribed degree of stability as additional performance indexes. The proposed method includes three parts: a residues approach for selecting the most effective signals to damp inter-area oscillation and designing phase-compensation parameters, a delay-dependent stability criterion to guarantee the asymptotical stability and an optimization algorithm to calculate the delay margin which constructs correlation between signal transmission delay, inter-area oscillation damping performance and prescribed degree of stability by transformation operation in complex plane. Case study is undertaken based on a four-machine two-area power system to demonstrate the design principle and verify the feasibility of the proposed method.