Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Bilateral Control Based on Equivalent Circuit Model

Haptic teleoperation has been studied as an attractive technology for a long time. Although the haptic teleoperation has been already realized by conventional methods, their physical meanings are remained vague and unclear. This paper explains bilateral control for the haptic teleoperation using an equivalent circuit model. Owing to the circuit model, which takes into account characteristics of electrical actuators, a control strategy and its physical meanings are clearly illustrated. Torque (force) control and position (velocity) control for the haptic teleoperation can be designed independently because of mode decomposition. Feedback controller design for each control is based on analyses of sensitivity derived from the equivalent circuit. The control strategy derived from the equivalent circuit have been confirmed by experiments and resulted in the haptic teleoperation.
Feasibility of a Gait Phase Identification Tool for Transfemoral Amputees Using Piezoelectric- Based In-Socket Sensory System

Gait detection is crucial especially in active prosthetic leg control mechanism. Vision system, floor sensors, and wearable sensors are the popular methods proposed to collect data for gait detection. However, in active prosthetic leg control, a tool that is practical in its implementation and is able to provide rich gait information is important for effective manipulation of the prosthetic leg. This paper aims to ascertain the feasibility of the piezoelectric-based in-socket sensory system that is hypothesized to be practical in implementation and provide sufficient information as a wearable gait detection tool for transfemoral prosthetic users. Fifteen sensors were instrumented to the anterior and posterior internal wall of a quadrilateral socket. One transfemoral amputee subject donned the instrumented socket and performed two walking routines; single stride and continuous walking. The sensors’ responses from both routines were analyzed with respect to the gait phases. The results suggested that the sensors output signal corresponds to the force components behavior of the stump while performing gait. All sensors were seen active during the first double support period (DS1). The anterior sensors were prominent during the initial swing (Sw), while posterior sensors were active during terminal Sw. These findings correspond with the muscle activity during the respective phases. Besides, the sensors also show significant pattern during single support and the second double support (DS2) phase. Thus, it can be deduced that the proposed sensory system is feasible to be used as a gait phase identification tool.
Comparative analysis of PI and PR based Current Controllers for Grid Connected Photovoltaic Micro-inverters

this paper presents a comparison between Proportional Integral (PI) and Proportional Resonant (PR) current controllers used in Single-phase Grid Connected Photovoltaic (PV) Inverters.The reference tracking with steady-state errors is enhanced with the second solution.The simulation results of the system shows that the PR controller with modified compensator has a good transient response compared to the conventional PI controller
A Feasibility Study on Piezoelectric MEMS Lateral Bulk Acoustic Wave Resonators including Thermal Effect

This paper presents a feasibility study on the design of piezoelectric MEMS lateral bulk acoustic wave resonators with particular reference to the thermal effect on the resonant frequency. This study compares the results obtained from both analytical models as well as finite element simulations with the temperature effects included. This comparative study has been carried out on resonators having a resonant frequency of around 19.5 MHz using two different Thin-Film Piezoelectric-on-Substrate (TPoS) MEMS processes: the first process consists of a 1 μm PZT layer over an SOI structure having a thickness of 5.5 μm, while the second process is the PiezoMUMPs MPW process consisting of a 0.5 μm AlN layer over an SOI structure having a thickness of 10 μm. Both the analytical and the finite element models indicate a frequency variation of 300 kHz over a temperature range of 273-573 K. Based on these results, a number of PiezoMUMPs resonator prototypes, including a thermal heating element, have been designed in order to explore the feasibility of fine tuning the resonant frequency using the thermal effect. The possibility of fine tuning can be applied to high precision timing circuits such as frequency counters.
Plasma Influences on the Performance of Ti Protected Back-Channel-Etched a-IGZO TFTs

A back-channel-etched process with Ti protective layer was used to fabricate the amorphous a-IGZO TFTs. To oxidize the Ti to $TiO_{2}$ on the back channel, kinds of plasma treatments were imposed to the Ti protective layer. The device with $N_{2}O$ plasma at 300°C in PECVD exhibits superior oxidizing effect, which is well compatible with large area production.
A High Speed High Voltage Normally-off SiC Vertical JFET Power Device

A high speed 3350V normally-off SiC vertical junction field-effect transistor (VJFET) power device is proposed in this paper. The gate-drain Miller capacitance of the proposed VJFET is greatly reduced in comparison with that of the conventional trenched-and-implanted JFET(TI-JFET), which results in a less than 25ns extremely fast turning-off speed. Meantime, the proposed SiC VJFET also has a low specific on-resistance with a value of 11.8 $\mathrm{m} \Omega \cdot \mathrm{cm}^{2}$ when $V_{\mathrm{GS}}$ is 2.6V.
A Fast MPPT Algorithm for Smart Grid-PV Connected System Based on Multiple Sparse-Aware Time-Varying Stepsize Adaptation Technique

The performance of solar photovoltaic systems integrated into the smart grid is dependent on utilization of the Maximum Power Point Tracking (MPPT) algorithm. In order to enhance the tracking ability, attaining stability, and terminating the possibility of misestimating a decreasing or increasing solar photovoltaic voltage, a multiple sparse-aware time-varying stepsize adaptation algorithm for the conventional MPPT method is proposed in this paper. The underlying purpose of this proposed technique, rather than using currently estimated power values, is to predict the power values of solar PV system to maximize efficiency and attain better performance. The prediction of power builds on an L-1 formerly estimated values of the PV voltage. Furthermore, the adaptation process is based on a time-adjusting stepsize that modifies according to the log value of the individual input voltage of the PV system to the adaptive filter. The proposed approach offers a fast-tracking capability and possesses a quick reaction to environmental alterations such as temperature and irradiance. Simulations show the superiority of the proposed technique in terms of supplying instantaneous tracking ability to the maximum power point, maintaining low steady-state error, and achieving a robust performance under rapid variations of atmospheric circumstances compared with standard algorithms.
Game Theoretic-Based Approaches for Cybersecurity-Aware Virtual Machine Placement in Public Cloud Clusters

Allocating several Virtual Machines (VMs) onto a single server helps to increase cloud computing resource utilization and to reduce its operating expense. However, multiplexing VMs with different security levels on a single server gives rise to major VM-to-VM cybersecurity interdependency risks. In this paper, we address the problem of the static VM allocation with cybersecurity loss awareness by modeling it as a two-player zero-sum game between an attacker and a provider. We first obtain optimal solutions by employing the mathematical programming approach. We then seek to find the optimal solutions by quickly identifying the equilibrium allocation strategies in our formulated zero-sum game. We mean by "equilibrium" that none of the provider nor the attacker has any incentive to deviate from one's chosen strategy. Specifically, we study the characteristics of the game model, based on which, to develop effective and efficient allocation algorithms. Simulation results show that our proposed cybersecurity-aware consolidation algorithms can significantly outperform the commonly used multi-dimensional bin packing approaches for large-scale cloud data centers.
Retinal based Automated Healthcare Framework via Deep Learning

Developing countries have limited access to low cost retinal images which hinders the progress in preventing needless blindness. Portable advancements are opening new doors to human services. Handheld ophthalmoscopes and smartphone fundus photography solutions are already providing promising solutions to low cost and portability. In this paper, we have discussed a new way to retinal fundus photography based on a Head Mounted Device(HMD) concept. HMDs will allow to obtain automated retinal images from patients. These images acts as biomarkers and helps in detection of chronic and long term diseases. Deep learning techniques are utilised to automate disease detection and patients are given corrective suggestions through their smartphones. We are translating technology into clinical care in the dawn of smart healthcare.
A New Differential Pressure Sensor Based Mass Flow Controller for Advanced Semiconductor Processing

A new type of pressure-based MFC has been developed based on a combination of absolute and differential pressure transducers. The differential pressure sensor provides a direct measurement of pressure differential across a flow restricting device such as a laminar flow element. This eliminates a requirement to have matched discrete pressure sensors across a flow restrictor to calculate accurate pressure differential. Another design consideration of the MFC is to locate the pressure sensors and flow restrictor upstream of the control valve to provide better immunity from downstream pressure fluctuations typically experienced in pulsed chemical vapor deposition applications, as well as achieve faster bleed down time of residual gases through the MFC. In addition, unlike conventional pressure-based MFCs, the differential pressure sensor-based MFCs can be operated at varying downstream pressure conditions- high vacuum and atmospheric pressures.
Finite Control Set Model Predictive Control of a Nine Switch Dual Output Converter as a Power Quality Conditioner

This paper presents a nine switch dual output (NSDO) converter topology as a power quality conditioner, using a finite control set - model predictive control (FCS-MPC) algorithm. A mathematical model is developed for the NSDO converter and is used in the FCS-MPC algorithm. The algorithm enables the NSDO converter to enhance the power quality of the grid, power control, and the DC-link voltage regulation. The performance of the model is validated through MATLAB simulations using non-linear and unbalanced linear loads. The simulation results show that the harmonics in the grid currents are well mitigated, and the DC-link voltage is regulated during severe load conditions. Moreover, the quality of grid currents agrees with the IEEE-519 standard.
Single-Tone Doppler Radar System for Human Respiratory Monitoring

Human respiration activities can be identified from the chest wall movement. In developing a non-contacting sensor for human respiration, the chest wall movement can be detected as a Doppler shift. Therefore, the Doppler radar is potential to be implemented for the non-contacting sensor previously mention. In this paper, the Single-Tone Doppler radar which operates at 10 GHz has been studied and proposed for detecting human respiration. The simulation experimental is performed for investigating the capability of the proposed method in detecting the human respiration parameter such as respiration rate and respiration amplitude. The results show that the proposed method is capable to extract the human respiration parameter.
The Application of Synthetic Neural Network in the Process Control Device

As a matter of an academic fact, modeling neuron is one of the most influential fundamentals in guiding and leading as well as the neural network efficiency, nonetheless the setting of the connections (Topology) in the network is a major role from affecting viewpoints. In this research the prediction of characteristics such as; temperatures, speeds and other parameters are as the outputs of an industrial process control device. First the outputs related to the speed, pressure and temperature are collected from the prepared tables of renowned corporations and then with the application of MATLAB software and the artificial neural network methods, a pertinent cliche for data is selectively collected. The final goal of this study is to attain the ideal regression which is recommended to be dealt with a Pre-assumption method. Besides, the final data calculated and sketched by graphs and diagrams were regulated then the variety of the mentioned methods in accord with their goals compared with one another and finally best ones of theirs chosen to be applied as the selective methods.
Input Current Ripples Cancellation in A New Three-level Neutral-Point-Clamped Bi-directional Coupled Quasi Z-Source Inverter

In this paper, the three-level (3L) neutral-point- clamped (NPC) bi-directional coupled quasi Z-source inverter (BCQ-ZSI) is proposed. The inductors of Z source network are coupled for the purpose of input current ripples cancellation. The detailed design of the coupled inductors have been given in terms of electrical and magnetic circuit constraints. Compared with the conventional 3L NPC QZSI, the proposed 3L NPC BCQ-ZSI can achieve completely input current ripples cancellation and also possess the capability of bi-directional power flow. The proposed 3L NPC BCQ-ZSI can avoid discontinuous conduction mode (DCM) with stable dc-link peak voltage under low power factor condition. Simulation results are presented to verify superiorities of the proposed 3L NPC BCQ-ZSI.
Identification and Compensation of periodic gear transmission errors in Robot Manipulators

Position and path accuracy are among the obstacles preventing robot manipulators to be more and more used for machining applications. Nonlinear joint errors are among different sources of error in robot positioning causing flactuational imperfections to a desired trajectory of TCP. In this paper we propose a novel method to identify and reduce the fluctuations in path of robot TCP resulting from the nonlinear behavior of joint transmission systems. This method is more time efficient compared to the rest of methods present in the literature. A practical example is presented in which we have reduced the fluctuation domain from 0.15(mm) to 0.09(mm) in an industrial robot arm.
EVALUATING PART-OF-SPEECH TAGGING AND PARSING On the Evaluation of Automatic Parsing of Natural Language

The aim of this chapter is to introduce the reader to the evaluation of part-of- speech (POS) taggers and parsers. After a presentation of both POS tagging and parsing, describing the tasks and the existing formalisms, we introduce general considerations about evaluation of Natural Language Processing (NLP). Then we raise a point about the issue of input data segmentation into linguistic units, a crucial step in any evaluation related to language processing. We conclude by a review of the current evaluation methodologies and average levels of perfor- mance generally achieved for POS tagging and parsing.
Biometric Recognition Using Multimodal Physiological Signals

In this paper, we address the problem of biometric recognition using the multimodal physiological signals. To this end, four different signals are considered: heart rate (HR), breathing rate (BR), palm electrodermal activity (P-EDA), and perinasal perspitation (PER-EDA). The proposed method consists of a convolutional neural network that exploits mono-dimensional convolutions (1D-CNN) and takes as input a window of the raw signals stacked along the channel dimension. The architecture and training hyperparameters of the proposed network are automatically optimized with the sequential model-based optimization. The experiments run on a publicly available dataset of multimodal signals acquired from 37 subjects in a controlled experiment on a driving simulator show that our method is able to reach a top-1 accuracy equal to 88.74% and a top-5 accuracy of 99.51% when a single model is used. The performance further increases to 90.54% and 99.69% for top-1 and top-5 accuracies, respectively, if an ensemble of models is used.
SMR-X: Flexible Parallel State Machine Replication for Cloud Computing

State Machine Replication (SMR) is a fundamental fault tolerant technique for distributed systems. SMR traditionally requires sequential execution of commands at each replica node, so as to guarantee strong consistency among replicas. To achieve high performance at large scale cloud datacenters, SMR has been parallelized by employing multiple threads at each replica. In this paper, we propose SMR-X, a novel parallel SMR scheme, which realizes flexible mapping of commands for parallel executing at each replica. The mapping between clients' requests and work threads is dynamically adjusted according to the load level of work threads. Therefore, workloads of different threads can be well balanced and high system throughput can be achieved. The major challenge in our work lies in the inconsistency problem caused by dynamic changes in request-thread mapping. To cope with this, we design delicate mechanisms to synchronize mapping function, so that strong consistency among replicas can be guaranteed. The correctness of the proposed scheme is rigorously proved and its performance is evaluated via simulations. Simulation results show that SMR-X can achieve better load balance and lower access latency than existing parallel SMR schemes.
Improved accuracy of model predictive control of Induction motor drive using FPGA

Finite control set model predictive control (FCS-MPC) is one of successful model predictive control approaches in electric drives which offers effective solution to multi variable multi criteria problems. The optimal control is found by “brute force” search over the limited set of possible control actions. Due to a discrete nature of power converters FCS-MPC is particularly well suited for use in electric drives. The performance of the control is closely related to accuracy of the model of controlled system. Conventional way of modeling electric drives is to include only simple model of the converter with ideal components with no voltage drops or effect of dead times. This simple mathematical converter description is computationally cheap enough to be implemented by conventional control hardware. On the other hand, the accuracy of the prediction is limited which may negatively impact the performance of the control. In this paper, we propose to design detailed mathematical model of the drive including the mathematical description of the inverter which allows us to address the problems associated with dead times and semiconductor voltage drops. Modeling those inverter non-linear effects can enhance the control accuracy especially in non-nominal drive conditions (e.g. low speeds). On the other hand the computational requirements increases. We propose to use FPGA to implement the control algorithm using fixed-point arithmetics with high level of pipelining resulting in very fast execution times while keeping FPGA resources low. The performance of proposed solution is verified by simulations and experiments on the laboratory prototype of induction motor drive.
A new interval source coding scheme over an ordered alphabet

In this paper, we propose an intermediate between the MTF and interval source coding schemes, which makes explicit use of alphabetical ordering as well as of symbol frequencies. The proposed scheme encodes the number of symbols that have appeared since the last previous occurrence of the current symbol, but counts only those symbols that are alphabetically bigger than the current symbol. The proposed method is capable of removing data redundancy due to symbols occurrence order.
Steps Towards Large Mylar Membrane Based Multiple Transducers, Application to Chemical Adsoptions Sensors

Integrated gas sensors and measuring systems are desired devices in many domains, including large scales applications. One of the main issue still is their poor chemical selectivity despite the very wide diversity of developed systems. In this paper, we present our approach based on selectively adsorbing zeolite thin films. A full instrument has been developed with up to five transduction means based on a single adsorbing layer of zeolite. That layer is suspended on a large Mylar membrane capped with a functionalized Aluminum thin film. We present also an analytical mechanical model that allows estimating the instrument intrinsic possibilities.
The Modified Parallelizable Iteration Algorithm for Signal Detection in Massive MIMO Systems

As one of linear signal detection algorithms in massive multiple-input-multiple-output (MIMO) system, minimum-mean-square-error (MMSE) algorithm can reduce the computational complexity at the expense of a small loss in detection accuracy. However, the complicated matrix inversion involved in MMSE algorithm leads to low parallelism. The paper proposes a novel algorithm named modified parallelizable iteration (MPI) algorithm. MPI algorithm approximately calculates the matrix inversion in an iterative way, where the initial solution by use of the characteristic of the MMSE filtering matrix. Simulation results show that MPI algorithm can achieve the near-optimal performance and high parallelism. Meanwhile, the computational complexity is further reduced one order of magnitude.
Trajectory Optimization for Legged Robots With Slipping Motions

The dynamics of legged systems are characterized by under-actuation, instability, and contact state switching. We present a trajectory optimization method for generating physically consistent motions under these conditions. By integrating a custom solver for hard contact forces in the system dynamics model, the optimal control algorithm has the authority to freely transition between open, closed, and sliding contact states along the trajectory. Our method can discover stepping motions without a predefined contact schedule. Moreover, the optimizer makes use of slipping contacts if a no-slip condition is too restrictive for the task at hand. Additionally, we show that new behaviors like skating over slippery surfaces emerge automatically, which would not be possible with classical methods that assume stationary contact points. Experiments in simulation and on hardware confirm the physical consistency of the generated trajectories. Our solver achieves iteration rates of 40 Hz for a 1 s horizon and is therefore fast enough to run in a receding horizon setting.
Loyola eCommons Loyola eCommons

— This paper proposes a new approach to the control of switching voltage regulators (buck converters). The method is performed using a switching-based clustering algorithm. The implementations of competing approaches such as a fuzzy-logic controller, proportional integral derivative controller and a neural network based controller are presented in order to compare and evaluate the performance of the switching-based clustering algorithm. The results of the approach show that the proposed method could improve the stability and the performance of the buck converter system by 2.7% in terms of settling time and by 0.6% in terms of the overshoot value as compared to other control methods for buck converters.