Miracle-dz/UniFAR-FTUs huggingface dataset:
Objective evaluation of resolution for low-light-level image intensifier based on dual-model

On the basis of an optical modulation model and a normalized correlation model,an objective evaluation method for the resolution of a low-light-level image intensifier was explored by using the dual criterion of template matching and optical modulation as core algorithms.A set of resolution objective evaluation system for the low-light-level image intensifier was constructed.In the method,the optical modulation model and the normalized correlation model are independent of each other and also cannot be separated from each other.Firstly,the template matching coefficients in the normalized correlation model were used to performed the location and preliminary evaluation,then the optical modulation model was taken to do a quantitative analysis.The image processing scheme in first qualitative analysis then the quantitative analysis improves the accuracy and repeatability of resolution test for the low-light-level image intensifier,implements the objective and accurate evaluation for low-light-level image intensifier and avoids the shortcomings of visual observation method which is influenced by human's subjective factors.The experiments demonstrate that the resolution of the image captured by CCD has good consistency with that of visual observation result by human eyes.The method is also suitable for the evaluation of resolutions of other visible light imaging systems.
Characterizing Crowdsourced Data Collected Using DESIM (Descriptive to Executable Simulation Modeling)

Across many case studies, the Descriptive to Executable Simulation Modeling (DESIM) method has demonstrated the ability to capture and model qualitative knowledge from multiple subject-matter experts (SMEs), convert those models to an executable form using a crowdsourcing approach, and interactively visualize the outputs. This method helps decision makers leverage collective expertise to perform complex “What if? ” analysis. This paper takes advantage of a large-scale multiple-model application of DESIM to illustrate the nature and interpretation of the data produced throughout its multiple phases. Lessons learned from this study provide direction toward future evaluation and improvements to this method.
Distributed naming in a factored operating system

A factored operating system (fos) is a new operating system design for manycore and cloud computers. In fos, OS services are separated from application code and run on distinct cores. Furthermore, each service is split into a fleet, or parallel set of cooperating processes that communicate using messages. Applications discover OS services through a distributed, dynamic name service. Each core runs a thin microkernel, and applications link in a user-space library called libfos that translates service requests into messages. The name service facilitates message delivery by looking up service locations and load balancing within service fleets. libfos caches service locations in a private cache to accelerate message delivery, and invalid entries are detected and invalidated by the microkernel. As messaging is the primary communication medium in fos, the name service plays a foundational role in the system. It enables key concepts of fos's design, such as fleets, communication locality, elasticity, and spatial scheduling. It is also one of the first complex services implemented in fos, and its implementation provides insight into issues one encounters while developing a distributed fos service. This thesis describes the design and implementation of the naming system in fos, including the naming and messaging system within each application and the distributed name service itself. Scaling numbers for the name service are presented for various workloads, as well as end-to-end performance numbers for two benchmarks. These numbers indicate good scaling of the name service with expected usage patterns, and superior messaging performance of the new naming system when compared with its prior implementation. The thesis concludes with research directions for future work. Thesis Supervisor: Anant Agarwal Title: Professor
Spatial Peripheral Interaction Techniques for Viewing and Manipulating Off-Screen Digital Content

When an information space is larger than the display, it is typical for interfaces to only support interacting with content that is rendered within its viewport. To support interacting with off-screen content, our work explores the design and evaluation of several spatial off-screen exploration techniques that make use of the interaction space around the display. These include Paper Distortion, Dynamic Distortion, Dynamic Peephole Inset, Spatial Panning, and Point2Pan. We also contribute a formalized descriptive framework of the off-screen interaction space that divides the around-device space into interaction volumes and analyzes them based on different factors. This framework guided the design of an off-screen interaction system, called Off-Screen Desktop, which implemented our spatial techniques using consumer-level motion sensing hardware. To enable a more detailed analysis of spatial interaction systems, we also developed a web-based visualization system, called SpatialVis, that visualizes log data over a video screen capture of the associated user interface.
Activity Recognition Research: The Good, the Bad, and the Future

Successfully recognizing people’s activities enables a wide range of pervasive computing applications. Recent research on activity recognition, particularly for elder care and health applications, has demonstrated that it is possible to recognize a variety of activities such as driving, walking, and using stairs and elevators [e.g. 2,5,6,9,10,13]. While we are inspired by existing activity recognition research, we believe as a community there are steps we can take together to enable future breakthroughs that are robust and reproducible. Our general interest in this workshop stems from our research in ubiquitous and pervasive computing. A.J. Brush’s main research interest is technology for homes and families, in particular supporting sharing, sustainability, and helping with everyday problems such as scheduling and coordination. John Krumm focuses on techniques for measuring a person’s location and for using location data to benefit the user. He has worked on predicting driving routes and destinations and looked for routines in logs of activity data. James Scott conducts research on sensors and devices, mobile interaction, energy management, security and privacy. Two current projects led to our particular interest in this workshop. First, as part of a study to understand arrival and departure prediction for households, we have been collecting GPS data from 12 households which we would like to provide to other researchers. This has required additional effort to collect and anonymize data to address privacy and legal concerns, which we would be interested in discussing with other members of the community. Second, in a project that involves recognizing activities using sensors on mobile devices, we are more interested in building experiences based on recognized activities than conducting foundational research on activity recognition ourselves. Ideally we would be able to build on previous activity recognition research, but we have not found this easy to do. In the rest of this paper, we describe what we think the community is doing well, places we believe
Towards incremental deep learning: multi-level change detection in a hierarchical visual recognition architecture

We present a trainable hierarchical architecture capable of detecting newness (or outliers) at all hierarchical levels. This contribution paves the way for deep neural architectures that are able to learn in an incremental fashion, for which the ability to detect newness is an indispensable prerequisite. We verify the ability to detect newness by conducting experiments on the MNIST database, where we introduce either localized changes, by adding noise to a small patch of the input, or global changes, by changing the global arrangement of local patterns which is not detectable at the local level.
Free-space experiments with MIMO UMTS high speed downlink packet access

The HSDPA (High Speed Downlink Packet Access) mode of UMTS provides high data rates to the user. In this contribution comparisons, based on free space measurements, between SISO (Single Input Single Output) HSDPA and MIMO (Multiple Input Multiple Output) HSDPA are shown. For two transmit antennas the STTD (Space-Time Transmit Diversity) mode of UMTS is used to increase the diversity order of the transmission. Measurements with four transmit antennas are carried out incorporating the DSTTD-SGRC (Double Space-Time Transmit Diversity with Sub-Group Rate Control) proposal of MERL (Mitsubishi Electric Research Laboratories). Besides of the comparison of the various HSDPA schemes, this paper focuses on the setup of the radio frequency frontend and its requirements for MIMO transmissions.
The Approximate Solutions of First Order Difference Equations with Constant and Variable Coefficients

In this paper a numerical method namely Homotopy Analysis Method (HAM) is presented to solve linear difference equations with variable coefficients under the mixed conditions. In addition, three numerical examples are given to make clear and illustrate the features and capabilities of the presented method. Then numerical results obtained by HAM are compared with the exact solutions.
Advanced Detection Schemes of Digital Signals in Impulse Noise

In this thesis, we investigate the optimum approach to mitigate the effects of impulse noise in wireless communication channels. First, we present a measurement campaign to verify the statistical properties of a Middleton Class-A (MCA) model for impulse noise. This campaign measures wireless interference that corrupted a 2.4 GHz industrial, scientific, and medical (ISM) band. We extend this verification to characterize the spatial coupling and correlation of impulse noise for multiple antenna systems. We then investigate the optimum detector for binary signals corrupted by MCA noise. We approximate the MCA model to a single weighted Gaussian density such that the nonlinearities of the optimum detector can be evaluated in a closed-form expression. The approximate MCA model discriminates the noise probability density function (PDF) of Gaussian and impulsive events using a threshold detection scheme. By means of such approximations, we provide a precise analysis for the behaviors of optimum nonlinearities in reducing the effects of impulse noise. We also introduce a decision boundary analysis to justify and analyze the performance of the optimum detector in different MCA noise environments. We also approximate the nonlinearities to investigate the behaviors of the other suboptimum detectors such as a locally optimum detector (LOD) and a clipping detector. As a suboptimum approach, we further approximate the optimum nonlinearities using linear segments to introduce new suboptimum detectors such as a piecewise linear detector and a clipping-like detector. Next, we extend the approximate MCA model to derive the optimum combining schemes for time and space diversity in the presence of fading and impulse noise. We assume perfect knowledge of noise states to evaluate the analytical performances of the optimum combining schemes for time, receive, and transmit/receive diversity in Rayleigh fading and MCA noise. Although this assumption is unrealistic, it leads to a tight performance bound for the optimum combining schemes in impulse noise. These evaluations allow us to study the performance loss of spatial diversity with respect to the number of transmit and receive antennas. Finally, we utilize the spectral dimensions in the mitigation problem of impulse noise for orthogonal frequency division multiplexing (OFDM) systems. We start with the receiver design of OFDM systems applied to fading channels with MCA noise, such as the optimum receiver and a conventional OFDM detector. We then evaluate an upper performance bound for the optimum receiver in flat fading with MCA noise. This analysis indicates a significant performance improvement of the optimum detector over a conventional OFDM detector in impulse noise. We finally develop a sphere decoding along with sparse Bayesian learning (SBL) to realize the optimum OFDM detector in MCA noise.
A dynamic non-singleton fuzzy logic system for DS/CDMA communications

We investigate a dynamic fuzzy logic system, specifically, its non-singleton generalization, and derive a novel adaptive fuzzy line enhancer that successfully solves the non-linear problem of narrowband interference (NBI) estimation and rejection in DS/CDMA for non-stationary environments. The NBI suppression performance of this method is compared with the performance of the linear and nonlinear estimator-substracter NBI suppression techniques for low and high signal to noise ratios (SNRs). It is seen that the proposed method outperforms all the previous nonlinear methods of NBI suppression whatever the SNR is, while almost attains the good performance of the linear ALE for low SNR and slow varying interference.
Methods for Geometric Accuracy Investigations of Terrestrial Laser Scanning Systems

Currently the second, or for some manufacturers even the third, generation of terrestrial laser scanning systems is available on the market. Although the new generation of terrestrial 3D laser scanning offers several new (geodetic) features and better performance, it is still essential to test the accuracy behaviour of the new systems for optimised use in each application. As a continuation of previously published investigations the Department Geomatics of the HafenCity University Hamburg (HCU Hamburg) carried out comparative investigations into the accuracy behaviour of the new generation of terrestrial laser scanning systems (Trimble GX, Leica ScanStation 1 and 2, and Riegl LMS420i using time-of-flight method, Leica HDS6000, Z+F IMAGER 5006, and Faro LS880 HE using phase difference method). The results of the following tests are presented and discussed in this paper: test field for 3D accuracy evaluation of 3D laser scanning systems, accuracy tests of distance measurements in comparison to reference distances, accuracy tests of inclination compensation, and influence of the laser beam’s angle of incidence on 3D accuracy.
Neural Correlates of Learning in Brain Machine Interface Controlled Tasks

Brain-machine interfaces (BMIs) were first imagined as a technology that would allow subjects to have direct communication with prosthetics and external devices (e.g. control over a computer cursor or robotic arm movement). Operation of these devices was not automatic, and subjects needed calibration and training in order to master this control. In short, learning became a key component in controlling these systems. As a result, BMIs have become ideal tools to probe and explore brain activity, since they allow the isolation of neural inputs and systematic altering of the relationships between the neural signals and output. I have used BMIs to explore the process of brain adaptability in a motor-like task. To this end, I trained non-human primates to control a 3D cursor and adapt to two different perturbations: a visuomotor rotation, uniform across the neural ensemble, and a decorrelation task, which non-uniformly altered the relationship between the activity of particular neurons in an ensemble and movement output. I measured individual and population level changes in the neural ensemble as subjects honed their skills over the span of several days. I found some similarities in the adaptation process elicited by these two tasks. On one hand, individual neurons displayed tuning changes across the entire ensemble after task adaptation: most neurons displayed transient changes in their preferred directions, and most neuron pairs showed changes in their cross-correlations during the learning process. On the other hand, I also measured population level adaptation in the neural ensemble: the underlying neural manifolds that control these neural signals also had dynamic changes during adaptation. I have found that the neural circuits seem to apply an exploratory strategy when adapting to new tasks. Our results suggest that information and trajectories in the neural space increase after initially introducing the perturbations, and before the subject settles into workable solutions. These results provide new insights into both the underlying population level processes in motor
Functional language analysis in teaching reading of history-related texts in EFL context

The integration of language and discipline-specific knowledge is widely acknowledged to play an important role in second/foreign language teaching. There is no denying that studying content subjects demands reading specific text genres, characterized by specialized lexico-grammar and discourse structure. Therefore, it is reasonable and highly justifiable for English language teachers to incorporate elements of content area reading into classroom instruction to make it more beneficial for language learners. The aim of the present paper is to report the findings an action research study conducted with a view to examining the issue of constructing and performing reading tasks based on selected history-related texts by adopting the principles of the functional language analysis approach, proposed by Fang and Schleppegrell (2008, 2010). The primary goal of the study was to investigate whether the techniques worked out by the advocates of this approach can be helpful to EFL learners in reading history passages, and, consequently, whether they can contribute to satisfactory comprehension gains by learners reading content area texts. The analysis of the data enabled the researcher to reach a number of conclusions concerning the most problematic aspects facing secondary school readers in dealing with distinctive features of history texts, their attitudes to reading this type of texts and their evaluation of the practices adopted to deal with discipline-specific texts.
Probabilistic Atlas Based Region Growing Method for Contrast Enhanced MR Imaging

AutomaticsegmentationofliverregionsfromabdominalMRimagesisoneofthechallengesincom- puter aided diagnosis (CAD) system. Probabilistic Atlas (PA) based methods are considered e ffi cient and robust in liver segmentation because the knowledge of distribution of object is incorporated in segmenting procedure. However, the estimation of the parameters of PA impacts the precise of segment results directly. In this paper, we proposed a novel method using region growing method with PA. We carried out experiments by the proposed method on 7 cases of abdominal contrast-enhanced magnetic resonance (MR) Images. The experimental results show that the performance of the method using region growing with probabilistic atlas is higher than that without probabilistic atlas.
Voltage and Current Sensor for State Estimation in Distribution Network with Generation

The penetration of Distributed Generation (DG) into the distribution networks has been increasing during the last decades. However, the increase of generation brings additional technical challenges for the Distribution Network Operators (DNOs). Thus, the integration of DG along with the improvement of the quality of service is driving the DNOs to actively control their networks. In order to achieve an effective control of the distribution network it is necessary to know the voltage across the network. Due to the lack of the real-time voltage measurers available onto the distribution network it would be necessary to estimate the voltage. Thus, as mentioned, the implementation of a state estimation method in distribution network is challenging due to the lack of real-time measurements available. The measurements considered in the distribution state estimation methods are predominantly off-time estimations of loads and therefore named pseudo measurements. However, estimate loads at the Low Voltage Switching Board are extremely difficult. To improve the quality of the distribution state estimation results, more real-time measurements are needed. However, the more measurements placed in the distribution networks the greater is the investment from the DNOs. Consequently, a trade-off between voltage accuracy, reliability and cost is required. Measurement placement method can be a solution to find the optimal compromise on the number of real-time measurement and consequent voltage accuracy. New types of measuring equipment such as voltage and current sensors can also help to reduce the initial investment costs. These sensors are considered in this work and are used for both protection and measurement functions. Also, these sensors can be directly connected to the communication equipment and thus resulting in a reduction of the number of system components and wiring. This paper presents both a method to place voltage and current sensors across the MV network and a distribution state estimation algorithm. The single-phase distribution state estimation algorithm determines the voltage magnitudes across the MV network and their associated accuracy. The state estimation algorithm integrates realtime current measurements from the current sensors at the network, the DG site and the MV busbar at the primary substation site. The distribution state estimation algorithm is based on the weighted least squares approach. To minimize the estimated voltage inaccuracy, the placement method determines the best location for a minimum number of voltage and current sensors. The results showed that current and voltage measurement together can improve the estimated voltage magnitude accuracy. The method to place voltage and current sensors along with the distribution state estimation algorithm may allow distribution network operators to actively control their network and thus increase the amount of generation onto their distribution networks with an improvement in power quality.
A Taxonomy Approach to Studying How Gamers Review Games

Background. Player-generated reviews of video games represent a large, rich, and under-explored source of data for exploring what makes for an effective game. Aim. We explore whether existing theory, in the form of a comprehensive gaming taxonomy, suitably captures the issues that players raise when they review games. Method. User-submitted game reviews were coded along the dimensions of the comprehensive gaming taxonomy to test the frequency of usage of each dimension. Results. We found some support for the use of the taxonomy, as four of nine taxonomy features were frequently present in game reviews. We also found support for other features of reviews not encapsulated by the Bedwell et al. (2012) taxonomy. Specifically, we found that players often reviewed video games: a) holistically; b) by comparing them to other games, game franchises, and other reviews; and c) by judging the value of games in terms of time, money, and effort. These results have implications for using game reviews for future research.
Cooperative Coevolutionary Genetic Algorithm For Water Resources Optimization Model And Its Application

To solve the complexity of multi-public sources water allocation, the optimization model using the multi-objective function and multi-constrained conditions of the cooperative coevolution genetic algorithm (CCGA) was proposed for water resources management. The objective function was the highest benefit to both economy and society, while the constraints included available water supply amount, water demand, water supply projects capacity, ecology protection and non-negativity. Water demand ratio parameters for each public source were proposed which could be optimized by CCGA. The case study in Suibianhe Region showed that the proposed model was capable of allocating water resource efficiently and can provide useful information for water resources integrated management.