Miracle-dz/UniFAR-FTUs huggingface dataset:
Survey on CSI-based Indoor Positioning Systems and Recent Advances

With the popularization of mobile devices, location based services (LBS) have received significant attention. Global Navigation Satellite System (GNSS) that widely used outdoors is unable to achieve high-precision positioning indoors, thus various indoor positioning methods are proposed for precise positioning. Wi-Fi based indoor positioning is widely used to provide accurate and efficient location services due to the convenience of hardware facilities. Channel state information (CSI) is an essential feature provided by Wi-Fi signals, which depicts the fine-grained features of channels, and attracts great attention in recent years. This paper comprehensively reviews the positioning technology based on CSI, and compares the primary positioning systems that makes great progress. This paper are mainly represented from two aspects: geometry-based positioning method and fingerprint-based positioning method. The former introduces angle-based method, range-based method, and the combination of both. The later mines the development process from traditional machine learning method to deep learning method. At the same time, this article also gives a brief overview of CSI’s multi-source fusion technologies. Finally, this paper summarizes the challenges and points out the trends in this rapidly developing field.
A SSVEP-Based BCI for Controlling a 4-DOF Robotic Manipulator

It is difficult to control high degree of freedom (DOF) robotic manipulator based on brain computer interface (BCI). This paper proposes a BCI system which utilizes steadystate visual evoked potential (SSVEP) for controlling a 4-DOF robotic manipulator. In order to elicit SSVEP responses, a stimulator panel circumferentially equipped with 12 LEDs as flickering sources is designed to provide visual stimuli. Applying canonical correlation analysis (CCA) method, elicited SSVEP responses can be decoded as movement commands. Furthermore, the novel commands mapping is designed for controlling end effector to move in 3D space in an efficient and accurate manner. Online experiments were carried out and three subjects participated in this study to perform move-grasp tasks in the simulation environment. Seven out of nine tasks were successfully completed within 50 trials with an average time of 156.71 seconds. The results show that it is feasible and efficient to control 4-DOF robotic manipulator with the proposed SSVEP-based BCI.
Winding Material Effect on High Speed Brushless Permanent Magnet Machines

Winding losses in Brushless Permanent Magnet (BPM) machines are typically, one of the largest loss contributions, especially in high speed applications such as e-chargers, compressors and traction. For this purpose, it is crucial to predict and take into account also the AC winding loss component, in order to reduce the total amount of stator winding losses. In this paper, a comparative study of aluminum and copper windings is conducted with the aim of mitigating the effect of the winding losses. The study is carried out considering two high speed machines. The first one is the main motor of an electric powertrain, developed for a fully electric motorsport application, whereas the second one is a high-speed turbocharger for an electric vehicle power boost application.
Modulated Binary Clique Convolutional Neural Network

Although Convolutional Neural Networks (CNNs) achieve effectiveness in various computer vision tasks, the significant requirement of storage of such networks hinders the deployment on computationally limited devices. In this paper, we propose a new compact and portable deep learning network named Modulated Binary Clique Convolutional Neural Network (MBCliqueNet) aiming to improve the portability of CNNs based on binarized filters while achieving comparable performance with the full-precision CNNs like Resnet. In MBCliqueNet, we introduce a novel modulated operation to approximate the unbinarized filters and gives an initialization method to speed up its convergence. We reduce the extra parameters caused by modulated operation with parameters sharing. As a result, the proposed MBCliqueNet can reduce the required storage space of convolutional filters by a factor of at least 32, in contrast to the full-precision model, and achieve better performance than other state-of-the-art binarized models. More importantly, our model compares even better with some full-precision models like Resnet on the dataset we used.
Predicting Future Performance based on Current Brain Activity: An fNIRS and EEG Study

A familiar construct in everyday life is the use of past performance to predict future performance of individuals such as human operators. Here, we propose that brain activity measured during cognitive task execution can be used for prediction of future performance of the same individual on the same task. We recorded multimodal wearable neuroimaging data (Functional Near Infrared Spectroscopy and Electroencephalogram) from twenty-three volunteers performing a cognitive task on three different days. We have analyzed the relationship of brain activity and behavior for both within and across sessions. Preliminary results across sessions show that, as expected, past performance is related to future performance during other sessions to an extent. However, brain activity captured during the task is a better predictor of the future performance compared to current performance. Moreover, within session results show that medial prefrontal cortex brain activity is correlated with imminent future performance as well. These are the first multimodal neuroimaging results suggesting that brain activity has macro (across days) and micro (across seconds) level links to performance.
Adaptive VSG Control Scheme for Large Scale Wind Farms to Improve Frequency Response Characteristics

The frequency support capability of the power grid is weakened with the increase of wind power penetration. To address this problem, wind farms (WFs) are required to participate in grid frequency response by power system operators. The WFs can participate in both inertia response (IR) and primary frequency regulation (PFR) by adopting conventional virtual synchronous generation (VSG) scheme that has fixed control parameters. Such design cannot accommodate different operation conditions of WFs and results in insufficient utilization of the energy stored in the VSG controlled WFs (VSG-WFs) for frequency response. In order to make full use of the energy stored in wind turbines (WTs) for improving frequency response characteristics, an adaptive virtual synchronous generator (AVSG) control scheme is proposed in this paper. The reserved power is used to support IR. Compared with the conventional VSG scheme, the wind turbine (WT) rotor kinetic energy (KE) in the WFs can be utilized more sufficiently by adjusting the inertia coefficient according to both WT rotor speed and grid frequency. The effectiveness of the proposed scheme is verified by simulations. It can be seen that during the same frequency event, both the rate of change of frequency (RoCoF) and frequency nadir of the power grid are improved with AVSG scheme compared with conventional VSG scheme.
Green distributed antenna systems for smart communities: A comprehensive survey

Smart communities are an emerging communication means in which humans and smart devices will interact with each other and deliver ubiquitous services by exploiting social intelligence. Distributed antenna system (DAS), one of the key technologies to realize smart decisions in smart communities, can settle network smart coverage problem and improve system energy/spectrum efficiency significantly. Considering that energy consumption is an important element for community communications, in this paper, we survey the existing green DAS research for smart communities. In particular, our investigation covers antenna distribution, system capacity, spectrum efficiency, energy efficiency, and green access issues. Moreover, we analyze the existing application opportunities and challenges. This survey contributes to better understanding of the challenges and approaches for green DAS in existing smart community networks and further shed novel light on some future research directions.
Autonomous Robotic Dialogue System with Reinforcement Learning for Elderlies with Dementia

The growing number of people living with dementia puts a heavy load on elderly care nurses. The use of humanoid robots as communication partners can help alleviate this load with the goal to assist seniors in maintaining their cognitive skills through conversation. In this paper we present an autonomous robotic dialogue system with the humanoid robot Bonoid that is capable of learning a conversation strategy from interactions with humans. The system was tested and evaluated with first results showing natural and engaging interactions with seniors, and the robotic system acquiring a conversation strategy through the use of reinforcement learning.
Effect of Dielectric Barrier Discharge Exposure on the Triboelectric Charging of Granular Plastics

The aim of the present paper is to study the feasibility of continuous dielectric barrier discharge (DBD) treatment of granular plastic materials in view of improving their triboelectric charging ability. The effects of the following DBD parameters were studied: the amplitude and the frequency of the applied high voltage, the thickness of the dielectric barrier, the distance between the electrodes of the reactor as well as the speed of the conveyor that was used for the transfer of the granular materials though the non-thermal plasma zone. The study was carried out on mm-size Polyethylene particles. It was found that the charge of DBD treated particles increases with the applied high voltage and decrease with the speed of the conveyor. Both the distance between the electrodes and the thickness of the dielectric barrier have a remarkable effect on the charge acquired after DBD treatment. Within the experimental domain of this study, the frequency of the high voltage does not significantly affect the efficiency of the tribocharging process.
Hybrid Model Predictive Control of Active-Neutral-Point-Clamped Multilevel Converters

To reduce the power losses, the high-voltage switches in active-neutral-point-clamped (ANPC) multilevel converters always operates at the fundamental frequency. Moreover, DC-link capacitor and flying capacitor voltage balance control is mandatory in addition to output current control in the ANPC converters. Model predictive control (MPC) can provide this flexibility and feature high dynamics for transient operations. However, conventional MPC experiences variable switching frequency, high total harmonic distortion. Moreover, as there is no modulator in the conventional MPC, it is very hard to guarantee the fundamental-frequency operation of the high-voltage switches. Given the issues above, a hybrid MPC is proposed to control the ANPC converter in this paper, where finite-control-set MPC is utilized for high-voltage switch control, and duty-cycle-optimized MPC is utilized for the low-voltage switch control. The proposed MPC features a fixed switching frequency and improves the steady-state performance with simplified implementation. The validation of the proposed hybrid MPC strategy is conducted on a five-level ANPC converter.
Electromagnetic disturbance of thermal measurements with the Vce(T)-method in power modules

This paper reports that the measurement of the thermal impedance of a power module via the Vce(T)-method is influenced by eddy currents in metal layers below the isolating ceramic. This influence is exemplified with measurements on a standard baseplate module. In these measurements, the apparent chip temperature evolution differs significantly if measured either at the auxiliary terminals or the main terminals. Experiments and simulations show good agreement and conclusively identify electromagnetic induction as origin of this difference. Finally, options to reduce the contribution of this effect to the measurement signal are discussed.
Development of an organic resistive-type humidity sensor

In this paper, the humidity sensing characteristics of an organic semiconductor Zn (II) tetra 5,10,15,20 phenyl-porphyrin (ZnTPP) have been investigated. The ZnTPP film was deposited by a spin-coating on glass substrate with pre-deposition of silver interdigital electrodes. The performance of the porphyrin humidity sensor was analysed through electrical resistance and conductance measurements at various humidity level. It was observed that the change in resistance at 20 Hz is about 816 times with increasing the relative humidity from 0% to 98% at 25°C. The response and recovery time of the sensor were about 80 and 130 s, respectively.
Short-term Vehicle Speed Prediction Based on Convolutional Bidirectional LSTM Networks

The optimal control based on the forecast of vehicle speed in the future is of great significance to vehicle safety system and energy management systems of hybrid electric vehicles. In this brief, a new vehicle speed prediction approach combining one-dimensional convolutional neural network with bidirectional Long Short-term Memory network (CB-LSTM), utilizing the information provided by V2V and V2I communication. Convolutional neural network (CNN) is used to receive input data and extract important features of the data, and bidirectional Long Short-term Memory network (Bi-LSTM) is used to receive the output of CNN layer, extract time series features, and produce final prediction results. The simulation results show that the prediction error increases with the increase of the prediction horizons, and the number of past values used in CB-LSTM has a certain impact on the prediction accuracy. Compared with the classical BP network, CB-LSTM has significantly improved the prediction accuracy for short-term vehicle speed prediction.
A Multi-Objective Task Reallocating Method in Complex Product Design Process Considering Design Changes

In the highly dynamic design environments, task allocation is subject to considerable design changes. The task reallocating to cope with various unexpected design changes becomes an increasingly important issue in the complex product design (CPD). In this paper, we propose a task reallocating model based on an adaptive multi-objective genetic algorithm and Tabu search (AMOGATS) method to study the dynamic task allocating procedure considering design changes, which integrates the advantages of adaptive genetic algorithm and Tabu search algorithm. Three objectives, i.e. completion time, robustness and stability, are considered simultaneously to measure the task allocation performance. A real example is employed to test and evaluate the performance of proposed method. The computational results imply that the proposed AMOGATS performs better than the heuristic algorithms available in the literature, which has more advantages on the convergence rate and running efficiency than the other algorithms, along with a better solution. This work provides a useful decision support to carry out the task reallocating with high levels of flexibility and efficiency during the process of CPD.
Research on FCS-MPC Control of LCL Grid-Connected Inverter Based on Optimized i1-i2-uc Prediction

By using multivariable model predictive control, the performance of predictive control can be effectively improved. In order to improve the quality of LCL grid-connected inverters based on FCS-MPC, a FCS-MPC control strategy of LCL grid-connected inverters based on optimized i1-i2-uC prediction is proposed. A mathematical model of LCL grid-connected inverters based on three variables prediction of grid-side current, inverter-side current and capacitor voltage in alpha-beta coordinate system is established. The switching vector optimization method is used to reduce the prediction calculation amount and switching loss. The performance of grid-connected inverters under grid balance and unbalanced grid is simulated and verified in detail. The simulation and experimental results show that the proposed control strategy course can effectively improve the quality of grid connection and reduce switching losses.
Probabilistic Lane Change Prediction using Gaussian Process Neural Networks

Finding patterns in the motion of surrounding vehicles is one essential challenge to master automated driving. It is necessary to estimate their future behavior to plan a safe and comfortable trajectory for the ego vehicle. Several approaches exist for this prediction task, where a large part of those methods use machine learning techniques. Due to the continuous motion of vehicles especially during lane changes, any kind of discrete prediction introduces the danger of a significant Bayes error, especially in the transition area from lane keeping to lane change. Therefore, this paper proposes to combine neural networks with Gaussian processes. While the neural network part works as an encoder to extract information from the input data and encodes it in a lower-dimensional space, the final Gaussian process part takes this information and treats it in a probabilistic fashion. The main advantage is that in this way not only a mean value is predicted that represents the lane change probability but a distribution of the prediction consisting of the mean and the standard deviation. So the future development of the traffic situation can be described in a more complex way, considering uncertainties in the input data which lead to an improved prediction performance. Therefore, in situations with an uncertain prediction a special maneuver could be performed by the ego vehicle to reduce the chance of an incident.
Online Traffic Speed Estimation for Urban Road Networks with Few Data: A Transfer Learning Approach

Online traffic speed data of urban road networks serve as the foundation of modern intelligent transportation systems. Much research has been conducted on developing methods, mostly model-based or machine learning ones, to estimate the data with GPS record for one, few adjacent roads, or the entire vehicular transportation network. While the machine learning methods generally yield satisfactory estimation accuracy, their accomplishments are established on a plethora of historical GPS records which may not be readily available for many urban transportation systems. In this paper, we investigate a transfer learning approach to provide speed data estimations with few data. We ground this work on a graph convolutional generative autoencoder that can generate the estimations for an entire transportation network in one go, and modify its internal computation graph to reduce the size of network topology-dependent model parameters. Subsequently, pre-trained models from road networks with massive historical data can be re-used in other networks with few data, which are only employed to adjust a small number of parameters. To assess the effectiveness of the proposed approach, comprehensive case studies are conducted, in which outstanding speed estimations can be obtained with significantly shorter training time.
New Two-to-Three-Phase AC-AC Indirect Matrix Converter with Open-end Rectifier Stage

A novel two-to-three-phase AC-AC indirect matrix converter with open-end rectifier stage is proposed. The proposed converter has no energy storage elements, which is implemented by only 14 IGBTs and 8 diodes. A new type of rectifier stage is proposed, thus creating a new family of matrix converters, entitled here as open-end rectifier stage. The rectifier stage is a X-Type current source rectifier (X-Type CSR), and the inverter stage is a typical three-phase three-legs two-level voltage source inverter. The modulation used is a space vector modulation (SVPWM), using the concept of the indirect matrix converter, which provides an ohmic relation in the input system, and zero current switching at rectifier stage. The converter proposed in this paper is verified by simulation, and provides input and outputs currents, and output voltages with low THD e WTHD. The prototype presented in this paper shows the possibility of building a converter with a reduced number of switches and high power density, with no DC-link energy storage element, with only small input filters, has being designed for a low cost microgeneration distributed energy system.
Morphological Landmark Detection on Lobsters Using Attention Networks

The aim of this paper is map landmarks on lobsters using convolution neural networks. An attention mechanism for improving the performance of the VGG network to that effect is used. Regular CNN architectures do not consciously extract detailed features from images. The attention mechanism learns to focus on regions around the lobster landmarks amongst the whole image. Existing approaches use a cascade of regression models for landmark prediction. Proposed, is a single iteration model augmenting an attention mechanism to produce similar results. The adaptability of the attention mechanism over any network, such as VGG16 or Resnet, avoids the need to learn the network from scratch.
Novel Active Nested Neutral-Point Piloted Nine-level Converter

This paper presents a new nine-level converter and its modulation for the medium- and high-voltage applications. The converter’s topology is based on the combination of the five-level active neutral-point-clamped (ANPC) and the neutral-point piloted (NPP) converters and is called the Nine-Level Active Nested Neutral-Point Piloted converter. The converter consist of three flying capacitors (FCs) per phase, which can be maintained at a nominal reference voltage using the converter’s redundant switching states. The proposed modulation is based on a phase-disposition pulse width modulation (PD-PWM) that incorporates a minimum energy property cost function along with a dynamic reference generation technique to improve the FCs voltage and the converter’s performance. Both the steady-state and transient performance of the converter have been investigated. The results indicate a high-quality output waveform and a sufficient capacitor voltage balancing control that guarantees a safe operation of the proposed converter. Additionally, the dc-link capacitor voltage ripple has been analyzed for all modulation indices and load power factors. The analysis shows a significant increase in the voltage ripple at high modulation indices.
Performance analysis of cooperative NOMA with energy harvesting in multi-cell networks

In this paper, an energy harvesting enabled cooperative non-orthogonal multiple access (NOMA) system for a multi-cell network is investigated. Particularly, during the direct transmission phase, base stations send their superposed messages to the near users and far users simultaneously according to a NOMA principle, while the near users act as energy harvesting enabled relays employing a power splitting protocol. During the cooperative phase, the near users transmit their decoded messages to the corresponding far users using harvested energy. Using tools from stochastic geometry, we firstly calculate the signal to interference ratios of the users in each NOMA group including one near user and one far user. Then, the closed-form expressions of the coverage probability, ergodic rate, and energy efficiency are derived respectively. Numerical results validate the derived expressions and show that the energy harvesting enabled cooperative NOMA system in a multi-cell network can improve the coverage probability, ergodic rate, and energy efficiency compared to its counterpart OMA system.