Miracle-dz/UniFAR-FTUs huggingface dataset:
Prediction-Error-Driven Position Estimation Method for Finite-Control-Set Model Predictive Control of Interior Permanent-Magnet Synchronous Motors

This paper proposes a prediction-error-driven position estimation method based on the current prediction errors of the finite-control-set model predictive control (FCS-MPC) for the position-sensorless control of interior permanent-magnet synchronous motors, which operate in the whole speed range, including zero speed. The method is customized for FCS-MPC with which switching devices are directly controlled without modulators, whereas the conventional sensorless control methods usually presume the use of modulators. In the low-speed region, the proposed method fully exploits the inherent excitation of the discrete switching of the FCS-MPC to extract the position information. Under the low-current condition, an active vector injection technique is designed to automatically maintain sufficiently rich excitation for the position estimation while exerting a minimum impact on the fundamental control. The resultant current ripples and acoustic noises are lower compared with conventional injection methods. A novel initial polarity detection method based on magnetic saturation effect and inductance identification is also proposed to prevent pole ambiguity. The high-speed region position estimation uses the excitation from the back electromotive force, which is also reflected in the current prediction errors, permitting the use of one simple linear estimator in the whole speed range. Finally, the effectiveness and benefits of the proposed method are validated by experimental results.
Editing of Library Metadata Records and its Effect on Subject Access: An Empirical Investigation

The research project some of the most interesting results of which are reported in this paper sought to evaluate the change in the sample of MARC 21 library metadata records over a period of two years. The study assessed various quantitative and qualitative aspects of subject metadata change over time in library metadata records. This research reveals that both 6XX and non-6XX MARC subject metadata fields have received a high level of attention from metadata editors as expressed in overall metadata change frequency and variety of 3 subject metadata change categories and 17 subcategories observed. Results suggests the most frequently occurring categories and subcategories of change within the subject metadata fields in library metadata records, the subject metadata fields most widely applied in the records, and the ones most often undergoing changes, as well as the fields in which multiple subcategories of change often co-occur. The study is expected to make contribution to our understanding of subject metadata role and place in library metadata records, as well as understanding of library metadata change.
An Unlinkable Authentication Scheme for Distributed IoT Application

The Internet of Things (IoT) is an enormous ubiquitous-network, which connects the objects through various sensors. The IoT technology promotes the interconnection and fusion between the physical world and information space, and it facilitates the day-to-day life of people. However, since a lot of equipped sensors are unattended and open, the IoT must face and overcome the main problems of security and privacy. Authentication is one of the paramount security concerns in the IoT environment, in which a user could directly access data from the sensors. Therefore, we propose an authentication and key agreement scheme providing unlinkability for the IoT environment based on bilinear pairings. The formal security proof demonstrates that the proposed protocol is unforgeable under the adaptively chosen message attack, and the session key exchange is semantic secure under the eCK model. In addition, the computation and communication costs of the proposed scheme are evaluated and compared with some existing similar schemes, which exhibits that it pleasantly addresses the needs of the IoT as far as security properties and computation expenses.
Analysis of Coding Strategies Within File Delivery Protocol Framework for HbbTV Based Push-VoD Services Over DVB Networks

Hybrid broadcast broadband TV is a technique providing Push-VoD services over an interactive hybrid TV. These services are broadcast using a file delivery protocol (FDP), which includes different coding strategies to ensure reliable delivery. This protocol is characterized by three levels of data representation giving rise to segmentation of packet losses, which may result in poor recovery capabilities. This paper provides a first thorough investigation of the coding FDP framework for reliable delivery of Push-VoD service over DVB networks. We propose Markov modeling for characterizing inter-layer loss propagation within FDP on a wide variety of burst erasure channels. Based on this analytical analysis and a simulation study, we determine the possible recovering areas and the accurate loss measurements within FDP. The latter is then used to effectively investigate and configure the different coding strategies provided within FDP. In addition, we present a suitable recovering strategy for FDP, which guarantees transmission robustness against the broadcast network impairments.
MOS Naturalness and the Quest for Human-Like Speech

This paper reconsiders the use of MOS naturalness as an instrument for measuring the quality (vs. intelligibility) of speech. We reconsider an earlier proposed alternative, the paired comparison or “AB” test, and present new empirical evidence that this is indeed a better method for evaluating TTS quality. Using this, we evaluate three older TTS systems along with a recent deep-learning approach against native North-American and Indian speech and show that, in fact, TTS had already crossed the threshold of human-like speech synthesis some time ago. This suggests that a systematic reappraisal of the concept of abstract “naturalness” of speech is in order.
Visual Space Perception

感覚生理学, 実験心理学において研究されてきた立体視覚に関する知見を, 将来のステレオ・テレビにそなえてテレビ技術者に必要と思われる範囲で解説したもの. 奥行知覚の種々の手がかりとその効果, 実体鏡をめぐる諸問題, 立体感の問題, 個人差の問題などが述べられる.
Semantic Failover in Software-Defined Networking

Software-defined networking (SDN) eases the management of large-scale network by providing centralized and programmable control of a network. The centralization inevitably creates a single point of failure and requires the use of redundant controllers. However, due to the need for replicating the SDN application states, existing solutions tend to assume that the controllers are of the same type. This imposes an undesirable trade-off between cost and availability as each active controller would require a dedicated standby controller of the same type. We propose semantic failover to address the issue, which allows generic failover across any types of controllers. Semantic failover models the SDN application states from the control plane messages and restores the application states by invoking the northbound API on the standby controller. It is thereby not dependent on specific types of controllers. The prototype system was tested on real-world SDN controllers, and the evaluation results have demonstrated the potentials of semantic failover for both homogenous and heterogeneous controller pairs.
A Survey on Multi-Sensor Fusion Techniques in IoT for Healthcare

In Internet of things (IoT) dedicated for healthcare, heterogeneous data can be gathered from different body sensors, environmental sensors and other data sources such as cameras, audio recorders, etc. The aggregation, synchronization, processing and fusion of these heterogeneous data are critical tasks to accurately provide real-time healthcare services. This paper provides a survey on different multi-sensor data fusion techniques in IoT for healthcare. Through focusing on decision-level fusion, the paper explains different advanced techniques such as machine learning that are needed for the integration of multiple healthcare data sources. Detailed comparisons of sensors used, healthcare applications, types of environment, accuracy metrics and results are discussed. In addition, we present observations and recommendations for researches who wish to work in sensor fusion for healthcare.
Finger vein recognition method and apparatus

The embodiment of the invention discloses a finger vein recognition method and apparatus to effectively extract finger vein recognition features for finger vein identification. The finger vein recognition method comprises steps of collecting a finger vein image, extracting an interesting area from the finger vein image via a linear fitting method, conducting geometric normalization and gray level normalization to the interesting area to achieve a processed area, determining a finger vein blood vessel line from the processed area to acquire a finger vein blood vessel image, and recognizing the finger vein according to the finger vein blood vessel image.
Skin Cancer Classification using Deep Learning and Transfer Learning

Skin cancer, specially melanoma is one of most deadly diseases. In the color images of skin, there is a high similarity between different skin lesion like melanoma and nevus, which increase the difficulty of the detection and diagnosis. A reliable automated system for skin lesion classification is essential for early detection to save effort, time and human life. In this paper, an automated skin lesion classification method is proposed. In this method, a pre-trained deep learning network and transfer learning are utilized. In addition to fine-tuning and data augmentation, the transfer learning is applied to AlexNet by replacing the last layer by a softmax to classify three different lesions (melanoma, common nevus and atypical nevus). The proposed model is trained and tested using the ph2 dataset. The well-known quantative measures, accuracy, sensitivity, specificity, and precision are used in evaluating the performance of the proposed method where the obtained values of these measures are 98.61%, 98.33%, 98.93%, and 97.73%, respectively. The performance of the proposed method is compared with the existing methods where the classification rate of the proposed method outperformed the performance of the existing methods.
An Analysis on Caching Placement for Millimeter–Micro-Wave Hybrid Networks

In this paper, we investigate the feasibility of <italic>wireless edge caching</italic> in a hybrid millimeter-wave (mmWave)–micro-wave (<inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>Wave) network. Considering the average success probability (ASP) of file delivery as the performance metric, we derive expressions for the association probability of the typical user to the mmWave and <inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>Wave networks using stochastic geometric tools. Accordingly, we provide an upper bound on the ASP of file delivery and formulate the content caching placement scheme as an optimization problem with respect to caching probabilities, which jointly optimizes the ASP of file delivery considering both content placement and delivery phases. To simplify the non-convex problem and obtain design insights, we split it into two scenarios: 1) noise-limited and 2) interference-limited, and then propose optimal caching placement algorithms for both. We numerically evaluate the performance of the proposed schemes under several essential factors, such as content popularity, cache size, target data rate, blockages in the mmWave network, BS density, and path loss and also compare it with other common proactive caching schemes, namely uniform caching, caching <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> most popular files, and random caching. Numerical results demonstrate the superiority of the proposed caching scheme over others, albeit certain trade-offs.
A Ka-Band Phase-Compensated Variable-Gain CMOS Low-Noise Amplifier

A variable-gain low-noise amplifier implemented in a 65-nm CMOS process for a beamforming front-end chip is presented, of which the phase remains constant during gain variations. The phase compensation characteristic is achieved by introducing a shunt PMOS and a parallel resistor at the differential outputs of common-gate (CG) transistors. This allows the gain to be controlled without phase variation by adjustment of the combined gate voltage of the CG transistor and the shunt PMOS at the same time. The proposed device shows a gain of 20.8 dB and a noise figure of 3.71 dB at 31 GHz. It shows a root-mean-square phase error of less than 3° over the gain control range of 10.6 dB at 30–34.5 GHz.
Dynamic Mental Model Construction: A Knowledge in Pieces-Based Explanation for Computing Students’ Erratic Performance on Recursion

The Knowledge in Pieces (KiP) framework offers a powerful perspective to understand the dynamic nature of knowledge and knowledge development. However, there is still much we do not know about the mechanisms underlying the moment-to-moment knowledge construction process. The purpose of this study was to explore the ways in which contextual knowledge elements support that process. According to KiP, a knowledge element, once activated, becomes part of the context that shapes the activation, configuration, and operation of other elements. Based on this assumption, we hypothesized that when the structural and operational characteristics of these contextual knowledge elements are compatible with those of the target concept, the contextual knowledge elements can support the knowledge construction process. This hypothesis was investigated in the domain of recursion, a fundamental but challenging programming concept. Sixty undergraduate computer science students completed four recursion evaluation tasks with varying likelihood to induce compatible contextual elements. Results showed that students performed better on high-compatibility tasks than on low-compatibility tasks at both the group level and individual level. Further qualitative analysis identified the knowledge elements involved and elaborated and refined the explanatory model for the contextual support effect.
Regrowth of a Large Intracranial Aneurysm after On-Label Use of the Pipeline Embolization Device

ABSTRACT Despite the widespread use of the pipeline embolization device (PED), no complete aneurysm regrowth after its placement has been reported in the literature. We report the first case of aneurysm regrowth after the initial follow-up angiography demonstrating near-complete occlusion of the aneurysm and remodeling of the vessel with on-label PED use for a large 20 mm × 24 mm × 22 mm (width × depth × height) cavernous segment internal carotid artery (ICA) aneurysm. The patient was treated with two overlapping PED (4.5 mm × 20 mm and 5 mm × 20 mm). Follow-up angiogram at 4 months after treatment demonstrated remodeling of the ICA with a small residual component measuring approximately 7 mm × 8 mm × 7 mm. However, at 10 months after treatment, there was a complete regrowth of the aneurysm with interval growth, now measuring 25 mm × 28 mm × 18 mm. Despite the high aneurysm occlusion rates reported with the PED, persistent aneurysm filling and aneurysm regrowth, although rare, should not be overlooked.
Dynamic Simulation of the HTS Maglev Vehicle-Bridge Coupled System Based on Levitation Force Experiment

In this paper, we built a high-temperature superconducting (HTS) maglev vehicle-bridge coupled system model by Universal Mechanism (UM) software, and analyzed the vertical dynamics. The UM model is composed of two parts, the train subsystem involved three vehicles, and the flexible bridge with simple-supports. In the UM modeling system, the expression of levitation force and the parameters related to the maglev vehicle-bridge were indispensable. The levitation force of maglev vehicle was described by an exponential analytical expression simplified by the experimental results of four YBCO bulks above a Halbach permanent magnetic guideway. The parameters related to the maglev vehicle-bridge are based on experimental prototype. Based on the UM model, the vertical dynamic was simulated and analyzed with different bridge spans under different operating velocities. This subject is a basic study for understanding the unique dynamic characteristic of the HTS maglev vehicle-bridge system. The simulation results provided reference for the further design of the HTS maglev vehicle-bridge coupled system in different speed ranges.
Real-time Smartphone Indoor Tracking Using Particle Filter with Ensemble Learning Methods

Location aware services in the Internet of Things are essential for smart environments. Location awareness enables operational systems to deliver useful information for supplying context-aware applications. We propose an efficient probabilistic model to provide good and stable localization accuracy in smart building environments for smartphones. Our proposed localization method fuses zone detection, radio-based ranging, inertial measurement units and floor plan information into an enhanced particle filter. Zone detection is designed with an ensemble learning algorithm by combining Hidden Markov Models and discriminative learning methods. We first apply ensemble learning models to achieve zone detection. Further, we integrate zone detection and an enhanced ranging model to achieve high and stable localization performance. Experiment results in an office-like indoor environment show that our system outperforms traditional localization approaches considering stability and accuracy. The localization method can achieve performance with an average localization error of 1.26 meters.
Detecting Student at Risk of Failure: A Case Study of Conceptualizing Mining from Internet Access Log Files

Predicting student academic performance can be done by using educational data mining. Machine learning techniques play an important role for predicting academic performance from the large-scale data like the internet access log files from a university. Current data sources are mainly manual collections of data or data from a single unit of study. This study highlights the use of a new data source by transforming a university log file to predict academic performance. The log file comprises student internet access activities and browsing categories. To detect overall student academic performance, we select the best prediction accuracy by enhancing two datasets and comparing different weights in the time and frequency domains. We found that the random forest technique provides the best way in these datasets to predict students at risk-of-failure. We also found that data from internet access activities reveals a better accuracy than data from browsing categories. The combination of two datasets reveals a better picture of students' internet utilization and thus indicates how students at risk-of-failure can be detected by their internet access activities and browsing behavior.
Behavior of the Grid Connected Converter in Unbalanced Smart Power Systems

Even with power electronic converters in almost every grid connected device, there are still several looming questions ahead, especially in regard to previously very rigid operating conditions. With the integration of non-linear consumers and inverter based distributed energy resources, the balanced nature of the power system may be impaired leading to asymmetrical voltages at the points of common coupling. This is particularly true for islanding and smart grid application. This paper aims at presenting the behavior of grid connected converter under asymmetrical voltages in unbalanced power systems. Detailed analysis of the asymmetrical voltage influence on key control aspects will be presented within the paper. The results for classically controlled grid connected converter operating under asymmetrical voltages at the point of common coupling will be presented. Special attention will be given to the influence of voltage asymmetry on important aspects of the control such as phase-locked loop and current controller.
Review of Graph Processing Frameworks

Data and data analysis tools have been increasing at a tremendous rate. Most of the data can be represented as graphs and therefore numerous large scale graph processing frameworks and systems have been proposed. Still, there is a daunting challenge to choose the best platform. We comprehensively survey all these frameworks, provide an in-depth taxonomy of more than 60 tools. We aim to help researchers in academia as well as industry with this work.
Electron Acceleration to MeV Energies at Jupiter and Saturn

The radiation belts and magnetospheres of Jupiter and Saturn show significant intensities of relativistic electrons with energies up to tens of megaelectronvolts (MeV). To date, the question on how the electrons reach such high energies is not fully answered. This is largely due to the lack of high‐quality electron spectra in the MeV energy range that models could be fit to. We reprocess data throughout the Galileo orbiter mission in order to derive Jupiter's electron spectra up to tens of MeV. In the case of Saturn, the spectra from the Cassini orbiter are readily available and we provide a systematic analysis aiming to study their acceleration mechanisms. Our analysis focuses on the magnetospheres of these planets, at distances of L > 20 and L > 4 for Jupiter and Saturn, respectively, where electron intensities are not yet at radiation belt levels. We find no support that MeV electrons are dominantly accelerated by wave‐particle interactions in the magnetospheres of both planets at these distances. Instead, electron acceleration is consistent with adiabatic transport. While this is a common assumption, confirmation of this fact is important since many studies on sources, losses, and transport of energetic particles rely on it. Adiabatic heating can be driven through various radial transport mechanisms, for example, injections driven by the interchange instability or radial diffusion. We cannot distinguish these processes at Saturn with our technique. For Jupiter, we suggest that the dominating acceleration process is radial diffusion because injections are never observed at MeV energies.
Using sequential analysis to assess component integrity of discrete-trial teaching programs

ABSTRACT Treatment integrity is the extent to which interventionists implement treatment procedures as prescribed. The majority of treatment integrity research involving discrete-trial teaching (DTT) reports an overall score summarizing integrity across procedural components. Because DTT is a procedure consisting of multiple steps, practitioners may find it helpful to report integrity data separately for each component (i.e., component integrity). Recent research has found that high levels of overall integrity are not always representative of high integrity for individual treatment components. The purpose of this study was to assess component integrity of DTT programs occurring in the natural environment with a specific emphasis on identifying integrity errors for an error-correction procedure consisting of remedial trials. The results showed that high overall levels of integrity did not correspond to high levels of integrity for the remedial trial portion of the error-correction procedures. We discuss the implications of the results for research and practice.