Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Pilot Allocation Strategies on Outage in Wireless Energy Transfer Using Massive Antenna Arrays

We investigate the viability of wireless energy transfer (WET) to multiple sensors using massive number of base station (BS) antennas based on estimates obtained through different uplink pilot signaling strategies, namely, orthogonal and shared. For the aforementioned strategies, we derive novel upper bounds on probability of outage in WET referred to as the probability that any sensor node fails to harvest a certain minimum amount of energy ${E_{u}}$ that it needs to send uplink pilots plus the energy $E_{p}$ that it needs to process its main tasks. We show how number of BS antennas scales with the number and position of sensor nodes, array transmit energy, channel estimation errors and nature of pilot signaling strategy employed. We prove when the sensor nodes are all equidistant relative to the BS, shared strategy gives identical outage probability as obtained through orthogonal strategy. However, when they are placed at different distances, shared strategy gives poorer outage performance than orthogonal strategy. To address this, we propose a simple location-dependent clustering and hybrid pilot assignment algorithm and also derive the corresponding probability of outage in WET. The proposed strategy elucidates an interesting trade-off between the outage performance that can be obtained and resources that must be spent on channel estimation.
Towards an IoT-based Deep Learning Architecture for Camera Trap Image Classification

Maintaining biodiversity is a key component of the United Nations (UN) “Life on Land” sustainability goal. Remote camera traps monitoring animals' movements support research in biodiversity. However, images from these camera traps are currently labeled manually resulting in high processing costs and long delays. This paper proposes an IoT -based system that leverages deep learning and edge computing to automatically label camera trap images and transmit this information to scientists in a timely manner. Inception-V3, MobileNet-V2, ResNet-18, and DenseNet-121 were trained on data consisting of 33,984 images taken during day and night with 6 animal classes. Inception- V3 yielded the highest macro average F1-score of 0.93 and an accuracy of 94%. An IoT-based system was developed that directly captures images from a commercial camera trap, does the inference on the edge using a Raspberry Pi (RPi), and sends the classification results back to a cloud database system. A mobile App is used to monitor the camera images classified on camera traps in real-time. The RPi could easily sustain a rate of processing 1 image every 2 seconds with an average latency of 1.8 second/image. After capture and pre-processing, each inference took an average of 0.2 Millisecond/image on a RPi Model 4B.
Similarity Preserving Unsupervised Feature Selection based on Sparse Learning

Various feature selection methods have been recently proposed on different applications to reduce the computational burden of machine learning algorithms as well as the complexity of learned models. Preserving sample similarities and selecting discriminative features are two major factors should be satisfied, especially by unsupervised feature selection methods. This paper aims to propose a novel unsupervised feature selection approach which employs an $\ell_{2,1}$-norm regularization model to preserve global and local similarities by minimizing an objective function. Cluster analysis is also incorporated in this framework to take the inherent structure of the data into account. The experimental results show the strength of the proposed approach as compared with the earlier well-known methods on a variety of standard datasets.
Target Tracking Control of a Wheel-less Snake Robot Based on a Supervised Multi-layered SNN

The snake-like robot without wheels is a bio-inspired robot whose high degree of freedom results in a challenge in autonomous locomotion control. The use of a Spiking Neural Network (SNN) which is a biologically plausible artificial neural network can help to achieve the autonomous locomotion behavior of snake robots in an energy-efficient manner. Approaches that use an SNN without hidden layers have been applied in the single-target tracking task. However, due to the complexity of the 3D gaits on a wheel-less snake robot and the imprecision of the pose control while in motion, they have some fluctuation that adversely affects their performances. In this work, we design two multi-layered SNNs with different topology for a wheel-less snake robot to track a certain moving object. The visual signals obtained from a Dynamic Vision Sensor (DVS) are fed into the SNN to drive the locomotion controller. Furthermore, the Reward-modulated Spike-Timing-Dependent Plasticity (R-STDP) learning rule is utilized to train the SNN end-to-end. Compared to the SNN without hidden layers, the proposed multi-layered SNN with a separated hidden layer shows its advantage in terms of robustness.
An Improved Drain-Current Model for FinFETs

In this paper, an improved charge-based drain-current model for FinFETs is proposed. The proposed model takes into account the effect of large dynamic changes in temperature. Based on the classic Poisson's equation, an exact charge density expression is proposed by using the Lambert W function. Compared with previous studies, more temperature-dependent terms in the expression of charge density are considered, which can characterize the change in charge density from normal temperature to low temperature. To validate the model, a 4-Fin FinFET with 16-finger in 14nm process is fabricated and the drain-current data was measured over a range of temperatures (84K-295K). Comparing the calculation results with the measured data, the maximum RMS error of drain-current characters has reduced from 0.2623mA to 0.0301mA.
A novel object detection algorithm based on enhanced R-FCN and SVM

Object detection is an extremely important part of computer vision. However, the object detection result of R-FCN is not good enough in terms of speed and accuracy. In this paper, a novel architecture called Enhanced R-FCN (ER-FCN) is proposed for object detection. Two improvements are presented in ER-FCN. Firstly, novel anchor boxes, 3 scales with box areas of 5122, 2562 and 1282 pixels, and 3 aspect ratios of 0.618:1, 1:1 and 1:0.618, are designed to suit the different scales object detection in RPN. Hence, the performance of object localization and detection speed are increased. Secondly, since the softmax classifier is not optimal to deal with the binary classification problem, a Whale Optimization Algorithm based on support vector machine, termed WOA-SVM, is introduced to improve the accuracy of classification. Extensive experimental results on PASCAL VOC 2007 and PASCAL VOC 2012 datasets show that the mean average precision of ER-FCN is improved by 3.9% compared with that of R-FCN.
Energy-Efficient Design for Massive MIMO With Hardware Impairments

In this paper, an energy-efficient design for massive multiple-input multiple-output (MIMO) systems is studied with the consideration of hardware impairments. The objective is to maximize the system energy efficiency of both uplink and downlink transmissions by jointly optimizing the number of antennas at the base station, the number of served users, and the transmit power. Firstly, by considering the linear distortion due to hardware impairments, the resultant channel estimation error from both distortion and noise is analyzed, following which closed-form approximations for the average achievable uplink/downlink rates are derived. Then, a massive MIMO energy efficiency optimization problem considering hardware impairments is formulated. By applying the techniques of relaxation and change of variables, an alternative optimization with build-in bisection search (AO-BS) algorithm is proposed with the quasi-concavity of the transformed objective function theoretically proved. The performance of the proposed AO-BS algorithm is validated through numerical simulations, which shows fast convergence to near-optimal solutions. Compared with existing approaches, the proposed scheme improves the system energy efficiency greatly when the hardware impairments are considered. Furthermore, the system design guideline in terms of the number of antennas, the number of served users, and the transmit power is provided.
Robust Micro-Particle Manipulation in a Microfluidic Channel Network Using Gravity-Induced Pressure Actuators

Robust particle manipulation is a challenging but essential technique for single-cell analysis and processing of microfluidic devices. This paper proposes a micro-particle manipulation system with a microfluidic channel network. We built gravity-induced pressure actuators, which can generate high-resolution output pressure with a wide range so that the multiple particles can be delivered from the inlet of the chip. In this paper, we studied how to model the proposed multi-input-single-output system and sources of disturbances, and designed a robust controller using disturbance observer technique. The performance of the proposed system was verified through experiments.
A Probabilistic Shared-Control Framework for Mobile Robots

Full teleoperation of mobile robots during the execution of complex tasks not only demands high cognitive and physical effort but also generates less optimal trajectories compared to autonomous controllers. However, the use of the latter in cluttered and dynamically varying environments is still an open and challenging topic. This is due to several factors such as sensory measurement failures and rapid changes in task requirements. Shared-control approaches have been introduced to overcome these issues. However, these either present a strong decoupling that makes them still sensitive to unexpected events, or highly complex interfaces only accessible to expert users. In this work, we focus on the development of a novel and intuitive shared-control framework for target detection and control of mobile robots. The proposed framework merges the information coming from a teleoperation device with a stochastic evaluation of the desired goal to generate autonomous trajectories while keeping a human-in-control approach. This allows the operator to react in case of goal changes, sensor failures, or unexpected disturbances. The proposed approach is validated through several experiments both in simulation and in a real environment where the users try to reach a chosen goal in the presence of obstacles and unexpected disturbances. Operators receive both visual feedback of the environment and voice feedback of the goal estimation status while teleoperating a mobile robot through a control-pad. Results of the proposed method are compared to pure teleoperation proving a better time-efficiency and easiness-of-use of the presented approach.
Richer Aggregated Features for Optical Flow Estimation with Edge-aware Refinement*

Recent CNN-based optical flow approaches have a separated structure of feature extraction and flow estimation. The core task of optical flow is finding the corresponding points while rich representation is just the key part of such matching problems. However, the prior work usually pays more attention to the design of flow decoder than the feature extraction. In this paper, we present a novel optical flow estimation network to enrich the feature representation of each pyramid level, with a hierarchical dilated architecture and a bottom-up aggregation scheme. In addition, inspired by edge guided classical methods, we bring the edge-aware idea into our approach and propose an edge-aware refinement (EAR) subnetwork to handle motion boundaries. Using the same decoding structure as PWC-Net, our network outperforms it by a large margin and leads all its derivatives both on KITTI-2012 and KITTI-2015. Further performance analysis proves the effectiveness of proposed ideas.
Learning Space-Time-Frequency Representation with Two-Stream Attention Based 3D Network for Motor Imagery Classification

Motor imagery (MI), as one of the important applications of brain-computer interface (BCI), has lately received great attention. However, current MI researches have not provided satisfactory representations of electroencephalogram (EEG), taking account of the space-time-frequency features for MI classification. Moreover, those models also lack the exploration of attentive spatial, temporal, and spectral dynamics. In this study, we propose TA3D (Two-stream Attention based 3D network), a novel model for MI classification. It mainly consists of two streams: the space-time stream and the space-frequency stream, representing and learning discriminative features in the space-time-frequency dimension. Specifically, each stream contains three key parts: 1) 3D representations of EEG signals depict the spatial information over temporal/spectral distributions; 2) Attention mechanisms adaptively explore attentive dynamics of EEG signals and focus on the most valuable information in separate dimensions; 3) 3D convolutions learn spatial representation, temporal dependence, and spectral dependence. The outputs of the two streams are concatenated for space-time-frequency feature fusion. Extensive experiments implemented on two BCI datasets demonstrate that our model outperforms state-of-the-art MI classification methods.
A Modular Hybrid Simulator for LoRaWAN

Low Power Wide Area Network (LPWAN) is considered as a prime choice for Internet of Things (IoT) connectivity as they provide low power data transmission over a wide range of communication. A simulation analysis gives an idea of how the deployed IoT network is going to behave and what kind of challenges it can face. A simulation-based study to assess the performance before deployment of IoT network is preferable.We have developed a discrete event, scalable, and open-source LoRaWAN network simulator, called Modular Hybrid Simulator for LoRaWAN. The script of the developed simulator is written in Python. The designed simulator consists of multiple modules for the required actions of LoRa communication. These modules are easy to understand and can be adapted to the application requirements. As the developed simulator deals with the IQ (in-phase and quadrature-phase) samples, it gives a better understanding of the LoRaWAN (MAC) and LoRa (PHY) interaction.
A Password Strength Evaluation Algorithm based on Sensitive Personal Information

Many Internet service providers are still using traditional password strength evaluation methods, resulting in user passwords being vulnerable to social engineering attacks. We believe that the password strength evaluation method based on sensitive personal information has great research value for improving the security of password authentication system. In this paper, we use the structure segmentation algorithm and the bidirectional matching algorithm to investigate how users' personal information is used in passwords. Then, we present a sensitivity personal information coverage evaluation function that represents the correlation between users' password and their personal information. Finally, a password strength evaluation method based on sensitive personal information is proposed. This method is composed of three stages: preprocessing stage, prediction dictionary generation stage and password strength evaluation stage.
Vision Global Localization with Semantic Segmentation and Interest Feature Points

In this work, we present a vision-only global localization architecture for autonomous vehicle applications, and achieves centimeter-level accuracy and high robustness in various scenarios. We first apply pixel-wise segmentation to the front-view mono camera and extract the semantic features, e.g. pole-like objects, lane markings, and curbs, which are robust to illumination, viewing angles and seasonal changes. For the scenes without enough semantic information, we extract interest feature points on static backgrounds, such as ground surface and buildings, assisted by our semantic segmentation. We create the visual global map with semantic feature map layers extracted from LiDAR point-cloud semantic map and the point feature map layer built with a fixed-pose SFM. A lumped Levenberg-Marquardt optimization solver is then applied to minimize the cost from two types of observations. We further evaluate the accuracy and robustness of our method with road tests on Alibaba’s autonomous delivery vehicles in multiple scenarios as well as a KAIST urban dataset.
Applications of Stretch Reflex for the Upper Limb of Musculoskeletal Humanoids: Protective Behavior, Postural Stability, and Active Induction

The musculoskeletal humanoid has various biomimetic benefits, and it is important that we can embed and evaluate human reflexes in the actual robot. Although stretch reflex has been implemented in lower limbs of musculoskeletal humanoids, we apply it to the upper limb to discover its useful applications. We consider the implementation of stretch reflex in the actual robot, its active/passive applications, and the change in behavior according to the difference of parameters.
Cognitive Vigilance Enhancement Using Audio Stimulation of Pure Tone at 250 Hz

In this paper, we propose a novel vigilance enhancement method based on audio stimulation of pure tone at 250 Hz. We induced two different levels of vigilance state; vigilance decrement (VD) and vigilance enhancement (VE). The VD state was induced by performing a modified version of the Stroop Color-Word Task (SCWT) for approximately 45 minutes. Likewise, the VE state was induced by incorporating audio stimulation of 250 Hz into the SCWT for 45 minutes. We assessed the levels of vigilance on 20 healthy subjects by utilizing Electroencephalogram (EEG) signals and machine learning. The EEG signals were analyzed using four types of entropies; Approximate Entropy (AE), Sample Entropy (SE), Fuzzy Entropy (FE), and Differential Entropy (DE). We then quantified vigilance levels using statistical analysis and support vector machines (SVM) classifier. We found that the proposed VE method has significantly reduced the reaction time (RT) by 44% and improved the accuracy of target detection by 25%, (p < 0.001) compared to VD state. Besides, we found that 30 min of audio stimulation has reduced the RT by 32% from the beginning to the end of VE phase of the experiment. The entropy measures show that the temporal profile of the EEG signals has significantly increased with VE. The classification results showed that SVM technique with DE features across all frequency bands can detect VE levels with accuracy varying between (92.10± 02.24)% to (98.32± 01.14)%, sensitivity of (92.50± 02.33)% to (98.66± 01.00)%, and specificity of (91.70± 02.32)% to (97.99± 01.05)%. Results also showed that the classification performance using DE has outperformed the other entropy measures by an average of +8.07%. Our results demonstrate the effectiveness of the proposed 250 Hz audio stimulation method in improving vigilance level and suggest using it for future cognitive enhancement studies.
Sorption of Antimony (V) onto Synthetic Goethite in Carbonate Medium

Abstract The sorption kinetics of antimony(V) on synthetic goethite is very fast compared to the sorption of other metals on goethite (e.g. arsenic and selenium) and depends on temperature, with an activation energy of 49±9 kJ · mol−1 in the temperature range 15–35°C. Sorption isotherms have been developed at different temperatures and ionic strength values. The results have been modelled using a Langmuir isotherm and there is not a considerable influence of neither the temperature in the range studied (15°C–35°C), nor the ionic strength (between 0.001 and 0.01 mol · dm−3). Sorption is very high at pH values lower than 8, at more alkaline pH, the sorption decreases with pH, as expected considering the Antimony(V) predominating complex in solution, Sb(OH)6 −. Triple‐layer model successfully describes the data obtained by assuming a bidentate edge‐sharing surface complex of antimonate on the surface of goethite.
Aspect-Based Sentiment Analysis of Individual Equipment Ergonomics Evaluation

The man-machine adaptability between soldiers and equipment affect the operational effectiveness of the whole individual soldier integrated system. In view of the complex semantic information and various equipment tendencies of individual soldier equipment evaluation, in order to extract key insights from soldiers’ comments on equipment, this paper constructs a Chinese multi-faceted and multi emotional (MME) dataset, and proposes an aspect-based sentiment analysis model. This model combines attention mechanism and context feature dynamic weighting method with global perceptual aspect embedding to enhance the BERT representation of aspect-based sentiment analysis. Then this study adds the offset correction to the ADAM algorithm, and finally maps the target to the correct context representation. The experimental results show that BERT’s dynamic weighting model has higher accuracy and score than MGAN, ATAE-LSTM, BERT-AEN, and BERT-Base models respectively in the classification of soldier equipment evaluation. It proves the effectiveness and feasibility of the data processing of soldier equipment evaluation.
A Novel Stacking Approach for Accurate Detection of Fake News

With the increasing popularity of social media, people has changed the way they access news. News online has become the major source of information for people. However, much information appearing on the Internet is dubious and even intended to mislead. Some fake news are so similar to the real ones that it is difficult for human to identify them. Therefore, automated fake news detection tools like machine learning and deep learning models have become an essential requirement. In this paper, we evaluated the performance of five machine learning models and three deep learning models on two fake and real news datasets of different size with hold out cross validation. We also used term frequency, term frequency-inverse document frequency and embedding techniques to obtain text representation for machine learning and deep learning models respectively. To evaluate models’ performance, we used accuracy, precision, recall and F1-score as the evaluation metrics and a corrected version of McNemar’s test to determine if models’ performance is significantly different. Then, we proposed our novel stacking model which achieved testing accuracy of 99.94% and 96.05 % respectively on the ISOT dataset and KDnugget dataset. Furthermore, the performance of our proposed method is high as compared to baseline methods. Thus, we highly recommend it for fake news detection.
Integration of Electric Vehicles in Smart Grid using Deep Reinforcement Learning

The vehicle-to-grid (V2G) technology provides an opportunity to generate revenue by selling electricity back to the grid at peak times when electricity is more expensive. Instead of sharing a contaminated pump handle at a gas station during the current covid-19 pandemic, plugging in the electric vehicle (EV) at home makes feel much safer. A V2G control algorithm is necessary to decide whether the electric vehicle (EV) should be charged or discharged in each hour. In this paper, we study the real-time V2G control problem under price uncertainty where the electricity price is determined dynamically every hour. Our model is inspired by the Deep Q-learning (DQN) algorithm which combines popular Q-learning with a deep neural network. The proposed Double-DQN model is an update of the DQN which maintains two distinct networks to select or evaluate an action. The Double-DQN algorithm is used to control charge/discharge operation in the hourly available electricity price in order to maximize the profit for the EV owner during the whole parking time. Experiment results show that our proposed method can work effectively in the real electricity market and it is able to increase the profit significantly compared with the other state-of-the-art EV charging schemes.