Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensorless Control for PMSM with Novel Back EMF Observer Based on Quasi-PR Controller

Sensorless Control of Permanent Magnet Synchronous Motor (PMSM) is a hot research topic nowadays. The main middle and high speed sensorless control methods are based on the back EMF model of the motor. Therefore, the back EMF observer plays an important role in the sensorless control system. The contribution of this paper is to propose a novel back EMF observer based on quasi-PR controller. The proposed quasi-PR observer has a simple structure similar to the sliding mode observer and eliminates the chattering phenomenon at the same time. In this paper, a quasi-PR observer is proposed to estimate the back electromotive forces (BEMFs), and a phase locked loop (PLL) is utilized to estimate the electrical speed and rotor position. The effectiveness is demonstrated by simulation and experimental results.
Compressive Classification from Single Pixel Measurements Via Deep Learning

Single-pixel camera (SPC) is a low-cost compressive imaging architecture that obtains random projections of the scenes using binary coded apertures. After the acquisitions, image reconstructions are usually obtained by nonlinear and relatively expensive optimization-based algorithms. Recent works have focused on designing the binary coded apertures to improve the speed of the reconstruction algorithms and the sampling complexity of the compressed sensing systems. However, it has been shown that image recovery is not necessary for image classification from compressive measurements, where only specific features of the images are required. This work proposes a deep learning approach for image classification directly from SPC measurements. In this approach, a neural network is trained to simultaneously learn the linear binary sensing matrix and the non-linear classification parameters, considering the constraints imposed by the SPC. Specifically, the first layer learns the sensing matrix, and subsequent layers perform the classification directly on the compressed measurements. Simulation results from two image datasets validate the proposed method, which provides the best classification accuracy along with a binary sensing matrix.
CPR Virtual Reality Training Simulator for Schools

This research project developed a Virtual Reality (VR) training simulator for the CPR procedure. This is designed for use training school children. It can also form part of a larger system for training paramedics with VR. The simulator incorporates a number of advanced VR technologies including Oculus Rift and Leap motion. We have gained input from NHS paramedics and several related organisation to design the system and provide feedback and evaluation of the preliminary working prototype.
Peak-Based Philips Fingerprint Robust To Pitch-Shift For Massive Audio Retrieval

An ideal audio retrieval system identifies a short query snippet from a massive audio database with both robustness and efficiency. Unfortunately, none of the existing systems could robustly handle all distortions while keeping efficient. An efficient audio retrieval method of the systems must match the features of the fingerprint. Enhanced Sampling and Counting method (eSC), the state-of-the-art audio retrieval method, proposed for Philips-like fingerprints, has achieved both high efficiency and strong robustness, featuring time-stretch resistance. We argue that Philips fingerprint, robust to many types of distortions except speed-change which includes time-stretch and pitch-shift, combined with eSC is promising towards an ideal audio retrieval system, if we could make it robust to pitch-shift. To achieve the goal, this paper proposes a peak-point based energy bands computation method (PPEB) to enhance Philips fingerprint (PF) with resistance to pitch-shift, and the resulting fingerprint is called Peak-point based Philips fingerprint (PPF). Experimental results show that PPF can resist pitch-shift ranging from 70% to 130%, while retaining the robustness of PF to various noise distortions.
A Partition Approach to Interpolate Polygon Sets for Animation

The use of animation can be a good alternative to static visualizations when communicating dynamic changes. Some approaches already represent spatiotemporal phenomena using a polygon set for each time instant. However, these representations are static and not general enough to be applied for the interpolation of arbitrary polygons. Furthermore, the problem of interpolating arbitrary polygons present a set of requirements that are not satisfied by the currently available tools. For example, the polygons are arbitrary, and the interpolation should be smooth and fully automatic (not requiring user intervention). To solve this problem, we present an approach to interpolate arbitrary polygon sets that satisfy those requirements, and that can be used to visualize temporal changes of different phenomena as an animation. In the proposed approach, we appropriately divide and identify correspondences between origin and target polygon sets. Our approach is general enough so that different polygon division techniques can be used. We also performed a series of experiments comparing a few different techniques and discuss the results.
The Nonlinear Compact Thermal Model of the Pulse Transformer

In the paper the compact nonlinear thermal model of the pulse transformer is proposed. This model has a form of the electronic network and it makes possible calculations of waveforms of the temperature of the core and every winding of the transformer taking into account both self-heating in each component of the transformer and mutual thermal couplings between these components, as well as nonlinearities of processes responsible for the removal of heat generated in the transformer. The form of the worked out model is presented together with analytic dependences describing influence of power dissipated in each component on self and mutual thermal resistances occurring in this model. Correctness of the worked out model is verified experimentally for selected constructions of pulse transformers.
MIMO Channel Estimation with Non-Ideal ADCS: Deep Learning Versus GAMP

Channel estimation for massive MIMO using coarse quantizers is nontrivial due to severe nonlinear distortions caused by quantization and the large dimensional MIMO channel. The best solutions to this problem nowadays are based on the generalized approximate message passing (GAMP) and its variations. However, there are practical issues such as nonideal quantizers that may violate the assumptions in which GAMP algorithms rely. This motivates research on methods based on deep learning (DL), which provides a framework for designing new communication systems that embrace practical impairments. We explore DL applied to channel estimation of MIMO systems with low resolution analog-to-digital converters (ADCs). Assuming millimeter wave MIMO, the channel estimation results indicate that a single neural network trained in a range of practical conditions is more robust to ADC impairments than a GAMP variant. For example, for a fixed wireless scenario with channels obtained from raytracing, DL achieved a normalized mean-square error lower than GAMP’s by more than 5 dB.
Self-Administered Screening for Syndemic Mental Health Problems Should be Routinely Implemented Among MSM PrEP Users

ABSTRACT Although literature has repeatedly shown that populations of men who have sex with men have (MSM) higher rates of mental health issues, including depression and addiction, routine screening is not part of standard care. This case series explores the value of implementing routine screening in a population of MSM before initiating HIV pre-exposure prophylaxis. We found the use of self-administered questionnaires for depression and substance use proved to be an acceptable and feasible method of assessment. Routine screening for syndemics in MSM populations is an important aspect of care that can have a significant impact on adherence and overall health outcomes.
Autoencoder Based Residual Generation for Fault Detection of Quadruple Tank System

Increasing complexity of industrial processes has made statistical methods for process monitoring and diagnosis a more attractive alternative to model-based methods. A primary reason is that statistical approaches can be formulated to rely less on process knowledge. Since multivariable processes can exhibit complex, nonlinear dynamics, there is a need for methods capable of diagnosing nonlinear process data. A Monte Carlo simulation was conducted on a numerical model of the quadruple tank process (QTP) - a novel multivariate nonlinear process. The simulation was designed so that the QTP exhibited bipartite nonlinear behavior. Reference data obtained from the simulation was used to obtain principal component analysis (PCA) and autoencoder (AE) models. The models generated residuals that were used to monitor the condition of the process. The results showed that AEs, which have nonlinear functionalities, performed better than PCA models at generating residuals.
VMRe: A Reverse Framework of Virtual Machine Protection Packed Binaries

Virtual machine protection is an advanced anti-reverse packing technology. This paper presents a framework for automatic analysis of binary packed by virtual machine protection software. Using multi-granularity dynamic instrumentation to trace binary can effectively record instruction execution and value changes in memory and register. Symbol execution is used to simplify assembly instructions and filter invalid instructions. The experimental results show that the method presented in this paper can effectively assist reverse engineers in the analysis of packed program by virtual machine protection.
PBC Design for Voltage Regulation in Buck Converters with Parametric Uncertainties

This paper addresses the problem of voltage output regulation in DC Buck converters from the passivity-based control (PBC) point of view. The PBC takes advantage of the natural port-Hamiltonian representation of dynamic equations of the buck converter, to design a feedback controller with proportionalintegral gains, that allows to guarantee stability conditions in the sense of Lyapunov for closed-loop operation. The design of the controller is based on the incremental dynamic model of the buck converter. The PBC approach considers unknown resistive loads in the controller design without degrading the dynamic performance of the controller. In addition, the proposed approach allows to design a controller regardless the buck parameters (capacitance and inductance) which makes it robust to parametric uncertainties. Sliding planes and classical PI control methods are used for comparing the proposed PBC method. All simulations have been performed in MATLAB software by using SymPowerSystems library.
Similar Seasonal-Geo-Region Mining Based on Visual Concepts in Social Media Photos

In this paper, we propose a method for similar geo-region mining focusing on the seasonality. We define "seasonal-geo-region" as a geographically and temporally continuous area where many travelers share common targets of interest. In order to extract such targets of interest, we consider that observing people's interests through the contents of social media photographs is an effective way. We first introduce a clustering method to decide seasonal-geo-region boundaries based on the geo-tag and shooting time accompanying a photo. Next, we introduce the proposed method that compares the similarity of a given pair of seasonal-geo-regions based on the likelihood distribution of Visual Concepts that appear in photographs belonging to each seasonal-geo-region. In the end, we introduce results of the seasonal-geo-region mining experiment and report an evaluation on the part of the results through a subjective experiment. The results showed the effectiveness of the proposed method.
Using Fully Convolutional Networks for Rumex Obtusifolius Segmentation, a Preliminary Report

Image segmentation of specific plants is an important task in precision farming. Several influences such as changing light, varying arrangement of leaves and similarly looking plants are challenging. We present a solution for segmenting individual Rumex obtusifolius plants out of complicated natural scenes in grassland from 2D images. We are making use of a fully convolutional deep neural network (FCN) trained with hand labeled images. The proposed segmentation scheme is validated with images taken under outdoor conditions. The overall masks segmentation rate is 84.8% measured by the dice coefficient. Approximately half of the experiments show segmentation rates of individual plants higher than 88%. The developed solution is therefore a robust method to segment Rumex obtusifolius plants under real-world conditions in short time.
SMAM: Detecting Rumors from Microblogs with Stance Mining Assisting Task

With the rapid development of the Internet, the Microblogging platform has become an important social media as well as an ideal platform for rumor spreading. Existing approaches mainly rely on the hand-crafted features which require daunting manual effort. Deep learning methods require less manual effort but none of them take full advantage of stance information. Stance information is an important feature for rumor detection because users exposed to rumors tend to express more querying and denying stances. A Stance Mining Assisting Model (SMAS) making full use of stance information is proposed in this paper. First, all the posts in a microblog event are divided into N subevents. Second, two deep learning network structures are proposed to learn hidden representations about stance mining and rumor detection respectively. Third, two hidden representations are concatenated into an integrated representation to detect rumor. The experimental results on dataset from Weibo platforms demonstrate that SMAS is effective in rumor detecting and the accuracy of our model achieves 93.9%.
Channel Doping Concentration Influence on BESOI MOSFET Light Sensor

The BESOI (Back Enhanced SOI) MOSFET is a new device that was developed and fabricated at the University of Sao Paulo in 2015. Its main advantages are the reconfigurable behavior, i.e., can work both like P- and N-type transistor depending only of the back gate bias, and fabrication simplicity. This paper studies the influence of channel doping concentration on the sensitivity of BESOI MOSFET used as a light sensor. The light sensitivity is investigated for different doping elements (boron and phosphorus) and it is shown that one impurity is more suited for a certain operation mode, i.e., pMOS and nMOS. An optimal doping concentration for better light sensitivity is obtained, resulting in $10^{\mathbf{17}} \mathbf{cm} ^{\mathbf{-3}}$ of boron for p-type $(V_{\mathbf{GB}} \lt \lt 0)$ and the same level of phosphorus for n-type $(V_{\mathbf{GB}} \gt \gt 0)$ BESOI MOSFET.
Knowledge Fusion: Introduction of Concepts and Techniques

The Internet contains a huge amount of data information, relying only on human resources can no longer obtain the complete and useful information from it. The research of knowledge graph provides a means to obtain large amounts of knowledge from massive texts and images, which plays an important role in people's daily life, such as Q&A, search, recommendation. The effective organization and integration of knowledge obtained from different sources into a knowledge base is the researching difficulty and hot spot to be solved in the knowledge graph construction in currently.
Sensorless Control of Permanent Magnet Synchronous Motor Based on Extended State Observer

For the position sensorless control of permanent magnet synchronous motor (PMSM) based on fundamental wave mathematical model, in order to detect the back electromotive force and estimate the position and speed of the rotor, the extended state observer (ESO) is used to observe the back EMF of the motor. According to the mathematical model of the permanent magnet synchronous motor in the two-phase stationary coordinate system, the second-order linear ESO is constructed to realize the observation of the extended back electromotive force (EMF), and then the rotor position and rotational speed are calculated by the inverse tangent function. Since the motor parameters are only used for arithmetic calculations and do not appear in the observation process, ESO has good parameter adaptability and robustness. In this paper, SIMULINK is used to simulate the performance and parameter adaptability of proposed method, and the effectiveness of it is verified.
On Matrix Momentum Stochastic Approximation and Applications to Q-learning

Stochastic approximation (SA) algorithms are recursive techniques used to obtain the roots of functions that can be expressed as expectations of a noisy parameterized family of functions. In this paper two new SA algorithms are introduced: 1) PolSA, an extension of Polyak’s momentum technique with a specially designed matrix momentum, and 2) NeSA, which can either be regarded as a variant of Nesterov’s acceleration method, or a simplification of PolSA. The rates of convergence of SA algorithms is well understood. Under special conditions, the mean square error of the parameter estimates is bounded by $\sigma^{2}/n+o(1/n)$, where $\sigma^{2} \geq 0$ is an identifiable constant. If these conditions fail, the rate is typically sub-linear. There are two well known SA algorithms that ensure a linear rate, with minimal value of variance, $\sigma^{2}$: the Ruppert-Polyak averaging technique, and the stochastic Newton-Raphson (SNR) algorithm. It is demonstrated here that under mild technical assumptions, the PolSA algorithm also achieves this optimality criteria. This result is established via novel coupling arguments: It is shown that the parameter estimates obtained from the PolSA algorithm couple with those of the optimal variance (but computationally more expensive) SNR algorithm, at a rate $O(1/n^{2})$. The newly proposed algorithms are extended to a reinforcement learning setting to obtain new Q-learning algorithms, and numerical results confirm the coupling of PolSA and SNR.
Nonlinear model predictive control of an overhead laboratory-scale gantry crane with obstacle avoidance

Gantry cranes are complex nonlinear electromechanical systems representing a challenging control problem. We propose an optimization-based controller for guiding the crane through arbitrary obstacles. Solving path planning problems with obstacles typically requires a two-stage approach. First, a path is found that is feasible w.r. t. system dynamics and obstacles. The path is then interpreted as a series of set points by a lower-level controller that guides the system. We instead generate a path, and the associated control input to move along that path, from a single optimization problem using a nonlinear model predictive control framework. In doing so, we generate a trajectory that is locally optimal and feasible w.r. t. system dynamics and obstacles. Multiple obstacle avoidance constraint formulations are proposed as smooth, differentiable functions. Objects are approximated either as the union of a set of smooth shapes or as smooth indicator functions. The formulations presented in this work are applicable to (non) convex problems in 2-D or 3-D spaces. Numerical methods are used to solve the proposed problems for both 2-D (fixed string length) and 3-D (varying string length) models of the gantry crane, resulting in consistently lower costs than nodal or sampling based algorithms.
Controlled Tactile and Vibration Feedback Embedded in a Smart Knee Brace

The vastus medialis oblique (VMO) and vastus lateralis (VL) muscles at the knee joint play important roles for coordination, stability, and posture control. However, no currently available device records muscle activity while simultaneously warning users regarding abnormal muscle activity. This study used a commercialized electromyography sensors placed on the VL and VMO to monitor muscle activity while triggering warnings and vibration feedback to the muscles when an undesired ratio of VL/VMO muscular activation level set by current study was detected. The system was able to trigger alarms when the subject performed moderate-intensity activity and avoid triggering during low-intensity activity. Further research is needed of high-intensity exercises, such as running or weightlifting, to determine the effects of increased muscle activity on the judgment of the undesired ratio of VL/VMO muscular activation level using the device reported in the current study.
A Novel Supervised Heterogenuos Feature Transfer Learning Scheme for ADL Recognition

In many of real-world applications of machine learning, one of the leading issues is the high cost of obtaining labeled data for learning tasks. One promising research area for solving this problem is transfer learning, which aims to learn a task in target domain by utilizing the training data in a different but related source domain. In this paper, we propose a novel supervised transfer learning algorithm that discovers good feature representations across different domains by deriving domain invariant feature space, where data distributions in different domains are close to one another. Within the new feature space, we apply classification algorithm to train classifiers in the source domain for use in the target domain. We verify the effectiveness of our approach by experimenting on real-world smart home datasets and shows that our approach extends the accuracy of transfer learning for activities of daily living (ADL) recognition.
Analysis of Electromagnetic Vibration Characteristics of An Interior Permanent Magnet Synchronous Motor

In this paper, the characteristics of electromagnetic force and vibration of a 50kW 48-slot/8-pole interior permanent magnet (IPM) synchronous motor are studied for the purpose of understanding its vibration mechanism. The electromagnetic force and resulting vibration of the IPM motor are evaluated with electromagnetic, and harmonic response finite element analysis (FEA) respectively. The vibration responses with two different methods of applying the electromagnetic force are also compared. It is concluded that simple lumped force excitation will bring inaccuracy when the spatial modes of the major excitations are too high. In addition, the in-plane vibration modes (modes without axial deflections) are identified as the major contributors to vibration response.
Exploring Mathematics Teachers’ TPACK Competency Development

The Technical Pedagogical and Content Knowledge (TPACK) framework was developed to measure teaching efficiency using information and communication technology (ICT). It was based on a literature review and research with 902 mathematics teachers in Beijing. In this study, mathematics teachers scored the highest in 'informational teaching belief' and the lowest in knowledge of student learning with integrated technology. K-means cluster analysis was used to classify teachers into five different types based on the framework. This research proposed a strategy to improve mathematics teachers' TPACK ability and promote ICT integration into education.