Miracle-dz/UniFAR-FTUs huggingface dataset:
High-time-resolution source apportionment of PM2.5 in Beijing with multiple models

Abstract. Beijing has suffered from heavy local emissions as well as regional transport of air pollutants, resulting in severe atmospheric fine-particle (PM2.5) pollution. This study developed a combined method to investigate source types of PM2.5 and its source regions during winter 2016 in Beijing, which include the receptor model (positive matrix factorization, PMF), footprint and an air quality model. The PMF model was performed with high-time-resolution measurements of trace elements, water soluble ions, organic carbon and elemental carbon using online instruments during the wintertime campaign of the Air Pollution and Human Health in a Chinese Megacity – Beijing (APHH-Beijing) program in 2016. Source types and their contributions estimated by PMF model using online measurements were linked with source regions identified by the footprint model, and the regional transport contribution was estimated by an air quality model (the Nested Air Quality Prediction Model System, NAQPMS) to analyze the specific sources and source regions during haze episodes. Our results show that secondary and biomass-burning sources were dominated by regional transport, while the coal combustion source increased with local contribution, suggesting that strict control strategies for local coal combustion in Beijing and a reduction of biomass-burning and gaseous precursor emissions in surrounding areas were essential to improve air quality in Beijing. The combination of PMF with footprint results revealed that secondary sources were mainly associated with southern footprints (53 %). The northern footprint was characterized by a high dust source contribution (11 %), while industrial sources increased with the eastern footprint (10 %). The results demonstrated the power of combining receptor model-based source apportionment with other models in understanding the formation of haze episodes and identifying specific sources from different source regions affecting air quality in Beijing.
Systematic design and prototyping of a disc-typed traveling wave ultrasonic motor

A systematic way in the design of disc-typed traveling wave ultrasonic motor based on the fundamental operating mechanism of piezoelectric disc was proposed. Four disc-typed prototypes were designed and fabricated. A test rig was also constructed for the evaluation of the prototypes and basic characteristics of the actuators were investigated. The working principle of the traveling wave motor was verified during the experiment using laser vibrometer. Basically, the optimal angular velocity of the prototype is dependent on the driving frequency, the input voltage, the contact surface and the friction coefficient between the stator and rotor. An analytical study of the prototypes has been carried out by means of finite element analysis utilizing ANSYS/spl reg/5.5. An equivalent circuit model is proposed for the estimation of the performance for the prototypes. Upon verification with the experimental results, we present a systematic method for the design of disc-typed ultrasonic motor according to the required specifications and application.
A Face Tracking Method Based on Camshift and SCKF in Rapid Moving Process

Aiming at the problem of face tracking under rapid moving process, a fast and robust tracking method is proposed. The possible position of face detected by the Camshift algorithm in the next frame is predicted by the square-root cubature Kalman filte (SCKF). Then, the localization and tracking of face are got frames by frames. The experimental results show that: the use of SCKF to solve the nonlinear effect caused by non-uniform motion of face and overcome the target loss problem of the linear Kalman algorithm. The proposed method greatly improves the tracking accuracy of face in the process of rapid movement.
Solving 3D anisotropic elastic wave equations on parallel GPU devices

Efficiently modeling seismic data sets in complex 3D anisotropic media by solving the 3D elastic wave equation is an important challenge in computational geophysics. Using a stress-stiffness formulation on a regular grid, we tested a 3D finite-difference time-domain solver using a second-order temporal and eighth-order spatial accuracy stencil that leverages the massively parallel architecture of graphics processing units (GPUs) to accelerate the computation of key kernels. The relatively small memory of an individual GPU limits the model domain sizes that can be computed on a single device. To circumvent this constraint and move toward modeling industry-sized 3D anisotropic elastic data sets, we parallelized computation across multiple GPU devices by using domain decomposition and, for each time step, employing an interdevice communication protocol to exchange data values falling within interior boundaries of each subdomain. For two or more GPU devices within a single compute node, we use direct peer-to-peer (i.e., GPU-to-GPU) communication, whereas for networked nodes we employed message-passing interface directives to route data over the network. Our 2D GPU-based anisotropic elastic modeling tests achieved a 10× speedup relative to an OpenMP CPU implementation run on an eight-core machine, whereas our 3D tests using dual-GPU devices produced up to a 28× speedup. The performance boost afforded by the GPU architecture allowed us to model seismic data for 3D anisotropic elastic models at lower hardware cost and in less time than has been previously possible.
Fuzzy image algebra neural networks for target classification

This paper presents a neural network application to target classification using a new type of neural network called the Fuzzy Image Algebra Neural Network (FIANN). The FIANN is used in a heterogenous network structure. The first layer of the net performs feature extraction, while the remaining layers are used for classification. Generalized image algebra operations are used to obtain fuzzy morphological or linear operation. The parameters for the generalized operations are learned in a fashion similar to standard backpropagation, but with training rules based on a combination of stochastic learning and gradient descent techniques. The type of data used is the range data part of tank LADAR data. The objective is to classify the tanks by type. The range data is first converted to elevation data, which is input to the net for feature extraction and classification. A two tiered approach is used for training. The first layer learns image features, while the top layers perform classification.
The Construction of the Knowledge Management System in the Cloud Computing Environment

In order to make the implementation of the KMS more easily and handle the knowledge more efficiently, we propose the KMS in the cloud computing environment. Firstly, we proposed the document, where the explicate knowledge exists in, processing method based on the Map reduce. Then the explicate knowledge can be processed parallel and faster. Afterwards we give the architecture of the KBS in the environments. The organization can rent the different kinds of services according to their requirements. Since the infrastructure is provided by the cloud computing providers, the organization can focus more on the functions of the KMS. It makes the implementation of the KMS more easily with lower costs.
Schematic driven module generation for analog circuits with performance optimization and matching considerations

A technology independent correct-by-construction module generation for analog circuits is described. The designer selects an arbitrary analog circuit partition in the schematic, and the procedure generates the corresponding layout as a optimal stack of transistors with complete intra-module connectivity. The matching requirements are used as the primary constraint along with considerations for parasitics, aspect-ratio, and area. For each of the modules, the port structures are also created for simplified routing. Corresponding to the selected circuit partition, a fully parameterized design rule independent module is generated. Any changes in the schematic and the design rules are automatically reflected in each of the modules. Results are demonstrated through a test chip.
Parallel Multicore CSB Format and Its Sparse Matrix Vector Multiplication

Sparse Matrix Vector Multiplication (SpMV) is one of the most basic problems in scientific and engineering computations. It is the basic operation in many realms, such as solving linear systems or eigenvalue problems. Nowadays, more than 90 percent of the world’s highest performance parallel computers in the top 500 use multicore architecture. So it is important practically to design the efficient methods of computing SpMV on multicore parallel computers. Usually, algorithms based on compressed sparse row (CSR) format suffer from a number of nonzero elements on each row so hardly as to use the multicore structure efficiently. Compressed Sparse Block (CSB) format is an effective storage format which can compute SpMV efficiently in a multicore computer. This paper presents a parallel multicore CSB format and SpMV based on it. We carried out numerical experiments on a parallel multicore computer. The results show that our parallel multicore CSB format and SpMV algorithm can reach high speedup, and they are highly scalable for banded matrices.
Current state of the art of vision based SLAM

The ability of a robot to localise itself and simultaneously build a map of its environment (Simultaneous Localisation and Mapping or SLAM) is a fundamental characteristic required for autonomous operation of the robot. Vision Sensors are very attractive for application in SLAM because of their rich sensory output and cost effectiveness. Different issues are involved in the problem of vision based SLAM and many different approaches exist in order to solve these issues. This paper gives a classification of state-of-the-art vision based SLAM techniques in terms of (i) imaging systems used for performing SLAM which include single cameras, stereo pairs, multiple camera rigs and catadioptric sensors, (ii) features extracted from the environment in order to perform SLAM which include point features and line/edge features, (iii) initialisation of landmarks which can either be delayed or undelayed, (iv) SLAM techniques used which include Extended Kalman Filtering, Particle Filtering, biologically inspired techniques like RatSLAM, and other techniques like Local Bundle Adjustment, and (v) use of wheel odometry information. The paper also presents the implementation and analysis of stereo pair based EKF SLAM for synthetic data. Results prove the technique to work successfully in the presence of considerable amounts of sensor noise. We believe that state of the art presented in the paper can serve as a basis for future research in the area of vision based SLAM. It will permit further research in the area to be carried out in an efficient and application specific way.
Forming valid scales for subjective video quality measurement based on a hybrid qualitative/quantitative methodology

This study presents a methodology of forming contextually valid scales for subjective video quality measurement. Any single value of quality e.g. Mean Opinion Score (MOS) can have multiple underlying causes. Hence this kind of a quality measure is not enough for example, in describing the performance of a video capturing device. By applying Interpretation Based Quality (IBQ) method as a qualitative/quantitative approach we have collected attributes familiar to the end user and that are extracted directly from the material offered by the observers' comments. Based on these findings we formed contextually valid assessment scales from the typically used quality attributes. A large set of data was collected from 138 observers to generate the video quality vocabulary. Video material was shot by three types of video cameras: Digital video cameras (4), digital still cameras (9) and mobile phone cameras (9). From the quality vocabulary, we formed 8 unipolar 11-point scales to get better insight of video quality. Viewing conditions were adjusted to meet the ITU-T Rec. P.910 requirements. It is suggested that the applied qualitative/quantitative approach is especially efficient for finding image quality differences in video material where the quality variations are multidimensional in nature and especially when image quality is rather high.
Statistics for Healthcare Professionals: An Introduction

Statistics for Health Care Research The Statistical Approach When Should It Be Applied? Measuring, Sampling and Error Questionnaires The Studies Descriptive Statistics Displaying Data Hypothesis Testing Distributions and Probabilities Making Predictions Testing for Differences between Means Errors and ANOVAs Not Normal Non-Parametric Tests Tests for Association: Chi-Square Tests for Association: Correlation and Regression Analyzing Data from Systematic Reviews Choosing Test Statistics
Performance of Random Access Scheduling Schemes in Multi-hop Wireless Networks

The performance of scheduling schemes in multi- hop wireless networks has attracted significant attention in the recent literature. It is well known that optimal scheduling solutions require centralized information and lead to impractical implementations due to their enormous complexity (high-degree polynomial or NP-hard, depending on the interference scenario). Further, an important characteristic in many multi-hop scenarios is the need for distributed algorithms that operate on local information. Thus, in this paper we develop a constant-time random access distributed algorithm for scheduling in multi- hop wireless networks. An important feature of this scheme is that it achieves a 2-approximation efficiency ratio and can be implemented in an entirely distributed manner with low overhead.
Interactive Preference Optimization for University Administrators

This paper describes a long-range planning procedure for administrators in private universities. The procedure operates by generating a sequence of alternative future configurations of the university that satisfy several financial constraints and by helping an administrator to assess his preferences on them. A mathematical programming algorithm is used to structure the interactive identification/search procedure. It incorporates the strategy of restriction and linear programming methods to generate new configurations, and it relies upon the interactively assessed preferences of the administrator to guide the search process rather than a standard objective function.
Monitoring of Drum-boiler Process Using Statistical Techniques

Present study addresses the monitoring of drum boiler process. Methodologies; based on clustering time series data and moving window based pattern matching have been proposed for the detection of fault in the chosen process. Design databases were created for the process by simulating the developed process model. A modified k-means clustering algorithm using similarity measure as a convergence criterion has been adopted for discriminating among time series data pertaining to various operating conditions. The proposed distance and PCA based combined similarity along with the moving window approach were used to discriminate among the normal operating conditions as well as detection of faults for the processes taken up. measures. The model has been derived using first principles and is characterized by few physical parameters. The parameters used in the model are those from a Swedish Power Plant. The values of all the parameters used were adapted from the work of K. J. Astrom & R. D. Bell. Sixteen (16) numbers of datasets belonging to four operating conditions including an abnormal operating condition were generated to evaluate the performance of the proposed techniques.
Reynolds-Averaged Simulation on Turbulent Drag-Reducing Flows of Viscoelastic Fluid Based on User-Defined Function in FLUENT Package

A new numerical simulation methodology for turbulent flows of viscoelastic fluid was developed for engineering application purpose based on commercial computational fluid dynamics code FLUENT package. An in-house subroutine was established and embedded into FLUENT code through userdefined function functionalization. In order to benchmark this methodology, numerical simulations on turbulent channel flows of viscoelastic fluid are conducted under different cases with drag reduction rates varied from low level to high level. FENE-P (finitely extensive nonlinear elastic-Peterlin) constitutive model is used to describe the viscoelastic effect of viscoelastic fluid flow. The turbulent model is developed in the framework of Display Formulak–e–ν′2¯–f model, for which the elliptic relaxation model is modified to account for the Reynolds stress equilibrium established by the presence of elasticity in the fluid. The numerical simulation results, including velocity profiles, turbulent flow characteristics, elastic stress and conformation field, show good agreements with published DNS results, which validates the newly established method on turbulent flows of viscoelastic fluid based on FLUENT software platform for engineering applications.Copyright © 2014 by ASME
Rate adaptive speech coding for universal multimedia access

This article reviews state-of-the-art in transport adaptation techniques for mobile networks. It discusses the mechanisms for rate adaptation to combat quality degradations of speech caused by the radio links. It begins with a review of dynamic schemes for adaptation of speech encoders in cellular networks where we observe two distinct approaches to rate adaptation: network controlled and source controlled. The issues associated with adaptive voice over IP (VoIP) mechanisms are considered next. Here, the encoder detects some form of network congestion to judge how to behave itself for the good of the network. It is noted that this altruistic behavior will only benefit coordinated IP networks such as private intranets and its application to the public Internet is improbable.
High-performance ECG signal acquisition for heart rate measurement

Signal processing has contributed to a new understanding of the ECG and its dynamic properties as expressed by changes in rhythm and beat. The analysis of the ECG has been widely used for diagnosing many cardiac diseases. Since the application of wavelets in biosignal processing is relatively new field of research, different methodological versions of the wavelet technique will require for further investigations to improve the diagnosis of abnormal signal. Developing an algorithm for the detection of the peaks in an ECG is a difficult problem due to the time-varying morphology of the signal subject to physiological conditions and the presence of noise. This paper presents an efficient R-peak detection algorithm for detecting peaks and physiological signal monitoring such as heart rate using LabVIEW. The experiment result indicates that ECG monitoring system for heart rate measurement can be implemented by using graphical programming language. It is a low-cost monitoring method and is easy to be popularised.
Rule Generation from Neural Networks

The neural network approach has proven useful for the development of artificial intelligence systems. However, a disadvantage with this approach is that the knowledge embedded in the neural network is opaque. In this paper, we show how to interpret neural network knowledge in symbolic form. We lay dawn required definitions for this treatment, formulate the interpretation algorithm, and formally verify its soundness. The main result is a formalized relationship between a neural network and a rule-based system. In addition, it has been demonstrated that the neural network generates rules of better performance than the decision tree approach in noisy conditions. >
Distance-constrained resource-sharing criteria for device-to-device communications underlaying cellular networks

A distance-constrained resource-sharing criterion (DRC) is proposed for device-to-device (D2D) communications underlaying cellular systems to mitigate the interference from cellular transmissions to the D2D link. The outage probability analysis and numerical evaluation reveal that there is an optimal minimum distance between the D2D receiver and its paired cellular UE, and the proposed DRC significantly reduces the outage probability of D2D communications.
Outside the cave of shadows: using syntactic annotation to enhance authorship attribution

This paper reports an experiment in authorship attribution in which statistical measures and methods that have been widely applied to words and their frequencies of use are applied to rewrite rules as they appear in a syntactically annotated corpus. The outcome of this experiment sug gests that the frequencies with which syntactic rewrite rules are put to use provide a better clue to authorship than word usage. Complementary methods focusing on the high-frequency head and the low-frequency tail of the distribution independently reveal a higher resolution than traditional word-based analyses, and promise enhanced accuracy for authorship attribution.
Massively Multiplayer Game Development

Massively Multiplayer (MMP) game development is one of the fastest growing areas in the game market. With the first MMP releases, the genre took off with amazing speed, and as its popularity continues to soar, so do the many challenges, internationalization, customer support, hacking, and technological advancements. To keep up with these challenges, developers need to implement the best tools and techniques available. Massively Multiplayer Game Development provides these solutions. This comprehensive, insightful collection of articles is written by some of the industry's best MMP developers, and provides a wealth of unique knowledge acquired while working in the MMP trenches. Details on database techniques and game systems are also included. Unlike many programming books, Massively Multiplayer Game Development is intended to appeal to the entire team: programmers will find a multitude of technical ideas, and designers and producers will benefit from the detailed information on design, architecture, and customer support.
Idle signal multiple-access (ISMA) scheme for terrestrial packet radio networks

This report proposes a new random-access packet communication system which is appropriately applied to terrestrial radio and called the idle signal multiple-access (ISMA) scheme. The characteristics of the system and its realization are studied. In this field, the CSMA system has received particular attention as a system with large channel capacity which can substitute for the ALOHA system. However, an assumption for this system is that all terminal pairs can observe each other's transmitted signals, thus imposing a severe requirement on a centralized system, which does not take into account direct communication between terminals. To cope with this problem, the BTMA system was proposed, but the ISMA system proposed herein shows its superiority to the BTMA system (a frequency-division method) in such aspects as channel capacity and structural simplicity because the entire frequency band can be used owing to the application of time division to the transmitted control signals. The channel capacity of the ISMA system is generally close to that of the CSMA system and it can be applied to a system operating with dynamic time division of channels for calls in both directions, upward and downward.