Miracle-dz/UniFAR-FTUs huggingface dataset:
Electromagnetic Tracking System Design for Location and Orientation Estimation

This paper presents an electromagnetic (EM) tracking system design for location and orientation estimations. Consisting of a sensing coil and a field generator formed by six excitation coils, the system tracks the sensing coil within the influence of the change in the magnetic field produced by the field generator. Closed-form solutions are formulated for the electromotive force (EMF) induced in the sensing coil, and the effects of translational and angular displacements are investigated. For the inverse calculation, artificial neural network (ANN) models are proposed to estimate the location and orientation of the sensing coil based on analytical EMFs. The closed-form solutions are verified by Finite Element Analysis (FEA), and the ANN models are also numerically validated. Experiments are conducted with a simplified prototype composed of one excitation and one sensing coil. The analytical model is first verified for both translational and angular displacements, and the proposed ANN models for inverse calculations are also validated by emulating the relative spatial relations between each excitation coil and the sensing coil.
DNN-based Active User Detection for an NB-IoT Compatible Grant Free NOMA System

Grant free non-orthogonal multiple access (GF-NOMA) is a promising access method for massive machine type communication (mMTC), which has several advantages when compared to the conventional grant based access method, such as, reduced latency, smaller scheduling and signalling overheads, and improved energy efficiency. Since there is no explicit grant given to each user in GF-NOMA, detecting all the active users present, i.e., active user detection (AUD) at the base station (BS) becomes crucial. Typically, AUD is performed using correlation with all possible preamble sequences transmitted by the GF-NOMA users. Recently, deep learning (DL) based models have emerged as a viable alternatives for AUD. However most of these works assume perfect timing and frequency synchronization of users, which hardly occurs in practice. In this work, a GF-NOMA scheme and a deep neural network (DNN) model for AUD are proposed. The proposed scheme is compatible with the narrowband Internet of things (NB-IoT) and the proposed DNN-based AUD mechanism accounts for the impact of timing and frequency offsets. It is demonstrated that the performance of proposed DNN based AUD scheme is comparable (or slightly better) than the conventional method while providing a significant reduction in the computational complexity.
Uplink Performance Analysis of Grant-Free NOMA Networks

Grant-free (GF) access is expected to support low-latency services in fifth-generation (5G) systems, while non-orthogonal multiple access (NOMA) has been proposed to enable massive connectivity in cellular networks. However, the performance analysis for the GF access mode based on NOMA is not trivial, especially for large-scale multi-cell networks due to the inherent random near-far phenomenon. In this paper, we exploit tools from stochastic geometry to develop a tractable framework for analysing uplink performance in large-scale multi-cell networks under GF NOMA and short packet transmission. To make the framework tractable, we further transform the intra- and inter-cell interference to an equivalent interference model. The URLLC performance of GF NOMA networks is derived under the assumption of perfect successive interference cancellation (SIC) and short packet transmission. Numerical results obtained from theoretical calculations and Monte Carlo simulations verify the correctness of our analysis.
A wireless Internet of Things tilting angle measuring system based on LoRa

For resolution the problem of tilting angle measurement of object at long distance, an embedded mobile angle measurement system based on multi-axis inertial sensors was designed. The system used magnetic, gyroscope, and acceleration multisensor to measure the tilting angle of object in the earth frame, and send the tilting angle data by LoRa wireless. This sensor system included one master device and several of slave sensor terminals. The motion data acquisition and tilting angle calculation were done by the slave sensors, and the master device was in charge of receiving, displaying data of multiple slaves, and managing the connection and breaking with slave sensor. The communication between the master device and slave sensors adopted LoRa wireless module, which was controlled by STM32 Cortex-M4 microcontroller. Finally, the experimental data showed this sensor system could accurately measure the tilting angle of object, and had 0.15 degree rms precision. By repeat testing, this sensor system ran stably, and could realize the tilting angle measuring at long distance for many motion applications.
V2E Association and Resource Allocation via Deep Reinforcement Learning in MEC-based HetVNets

Mobile edge computing (MEC) based heterogeneous vehicular networks (HetVNets) can interwork between IEEE 802.11p-based vehicular networks and cellular-assisted vehicular networks for vehicle-to-everything (V2X) communications. It is an attractive technology for supporting low latency applications for vehicles. However, in the practical system without precise prior knowledge of the dynamic wireless environment, solving joint vehicle-to-edge (V2E) association and resource allocation problem is a challenge. In this paper, first, we use stochastic geometry to model a real scenario. Specifically, the intersection area is modeled as two perpendicular streets, the spatial distribution of vehicle nodes on each street is modeled as an independent one-dimensional (1D) homogeneous Poisson Point Process (PPP), the spatial distribution of different types of edge nodes is modeled as different and independent PPPs. We consider the service time during which a vehicle node with different types of network interfaces gets a service from an edge node. Then, a deep reinforcement learning (DRL) based method is proposed to solve the uplink-and-downlink V2E association problem minimizing the service time while ensuring the computation resource allocation constraints. Simulation results illustrate the better performance of our solution than that of other traditional methods.
Improving Monaural Speech Enhancement with Dynamic Scene Perception Module

Speech enhancement aims to recover clean speech from complex noise backgrounds. This paper proposes a novel information processing module dubbed dynamic scene perception module (DSPM) that can help existing systems to accommodate various complex scenarios. The inspiration of DSPM is based on the observation that different regions of the noisy spectrum in different scenarios have different enhancing requirements. Concretely, DSPM consists of two parts, one for dynamic scene estimation, and the other for adaptive region perception. In particular, the scene estimator utilizes a spectrum-energy-based attention mechanism to obtain the coefficients of each convolution kernel. Then, at each position’ the region perceptron chooses the corresponding kernels by considering the requirements of the current region (preserve vocals or suppress noise). Systematic evaluations on the TIMIT corpus and Voice Bank + DEMAND demonstrate the effectiveness of our method. Compared with the existing systems, our proposed method achieved better performance under various SNR conditions and complex noise scenarios.
Jellyfish Grasping and Transportation with a Wire-Driven Gripper and Deep Learning Based Recognition

Automation has been adopted and realized in many industrial fields in recent years. However, it has been barely implemented in the field of dealing with living creatures. Therefore in this paper, we propose a robotic system for grasping and transporting jellyfish to automate operations involving living creatures. We created a wire-driven robotic gripper specialized for grasping jellyfish that is very soft with an extremely low friction coefficient. We trained a YOLOv5 model for recognizing jellyfish and obtain the grasping position. In experiments, we used gel jellyfish fabricated using a 3D gel printer instead of living jellyfish. Experiments were conducted to evaluate grasping range and height of the gripper. Results indicated that the grasping height limit was 15 mm from tank bottom, and the grasping range was a circular area with a diameter of 140 mm. Finally, we conducted an experiment to automatically detect, grasp, and transport jellyfish from one tank to another with the proposed robotic gripper and deep learning based recognition method.
Infrared Thermal Imaging Face Recognition Method Based on Temperature Block Feature

In order to prevent COVID-19 effectively, non-contact body temperature measurement and human identification are required in public places, but face recognition based on visible light cannot meet the requirements. Therefore, this paper proposes a thermal imaging face recognition method based on temperature block feature extraction. Histogram equalization and median filter are used to preprocess the face image, and Sobel operator is used for face detection; Six dimensional features including temperature mean, standard deviation and adjacent difference are extracted from each temperature block in the average poolinged temperature matrix, and classified by max-correlation-coefficient method. The experimental results show that the recognition rate of this method is 6.1% higher than that of PCA method with the temperature block size of $\boldsymbol{2\times 2}$. When using the same hardware to execute the program, if the two recognition rates are very close, the average test time of the proposed method is 22.2% less than the one of deep learning models such as Alexnet. Furthermore, the proposed method has strong robustness for small training sample set. For example, the recognition rate of single training sample model can reach 0.7, while in the deep learning model, except Mobilenet can reach 0.6, all of the others are less than 0.4.
MIMO Hybrid Beamforming for Line-of-Sight Interference Channels

Baseband adaptive array processing is a traditional approach to mitigating interference at a digital receiver. In the presence of sufficiently strong interference, this mitigation fails if receiver (RX) front-end components, such as amplifiers and ADCs, are driven into non-linear operation or if the RX responds by decreasing its gain and losing sensitivity to the signal. Instead of requiring costly RX components with high dynamic-range, we explore a novel Multiple-Input Multiple-Output (MIMO) scheme that uses hybrid analog-digital beamforming to mitigate a strong interferer in wideband line-of-sight (LOS) channels. This scheme uses true-time delay analog beamforming within antenna subarrays to mitigate most of the interference ahead of the RX front-end and then linear spatial equalization to mitigate residual interference at baseband. Alamouti space-frequency coding at the transmitter and MIMO receive processing are used to exploit the frequency-selective channel imposed by the analog beamforming stage. We show that this architecture can outperform traditional Single-Input Multiple-Output (SIMO) schemes under strong interference using low complexity processing in a relatively low-complexity hardware implementation. The specific case of inter-vehicular (V2V) LOS channels is considered in detail.
Multiple Carrier UAV Path Planning Based on Hybrid A*

Unmanned aerial vehicles (UAVs) are becoming more and more important in modern warfare, and future carrier UAVs will also be indispensable for the improvement of aircraft carrier combat capabilities. Taking into account the future trend of unmanned combat and the ease of dealing with the environment such as path planning in the hangar, the carrier UAV in this article needs to consider reversing motion. Based on this, a mathematical model for path planning is established and a hybrid A-star algorithm is introduced. The path planning problem of shipborne UAV. In addition, the previous multi-carrier UAV path planning did not consider the impact of the dispatch process, so this paper proposes a conflict avoidance strategy for multi-carrier UAV path planning. The simulation results show that the hybrid A* is effective in the path planning of carrier UAVs, and the proposed multi-carrier UAV conflict avoidance strategy can fully meet the given constraints. It shows that the method proposed in this paper can effectively cope with the path planning task of carrier UAV.
Volumetric Instance-Level Semantic Mapping Via BlendMask

Advanced tasks such as planning and scene interaction for autonomous robots require a detailed instance-level semantic map of the environment. To this end, this paper proposes a new volumetric instance-level semantic mapping approach, in which BlendMask is introduced as the instance segmentation algorithm for RGB images. As a result, improvements in the quality and speed of the instance segmentation are observed. Specifically, the geometric segmentation for the depth image and the instance segmentation results are fused together to construct the geometrically and semantically unified instances. Then, cross-frame instances are tracked and matched through data association. Based on the above, a global instance-level semantic map is constructed. Comparative experiments on public datasets are conducted to show that the proposed instance-level semantic mapping approach can effectively improve the instance segmentation effect and the quality of the constructed instance-level semantic map.
Ensemble-Based Distributed Learning for Generative Adversarial Networks

The deployment of generative adversarial networks (GANs) in wireless networks faces three key challenges of limited devices’ computational capability, scarce communication resources, and severe data privacy leakage. To address these issues, this paper proposes a new distributed framework for training GANs based on ensemble learning. First, multiple discriminators are trained at many devices using their local datasets. A generator is then trained at a central server by aggregating devices’ discriminators in an ensemble manner. The per-round training time is established. Finally, simulation results show that the proposed framework can simultaneously reduce the training time and improve the learning performance as compared with an existing framework.
Mining Image Semantics via Deep Learning: A Robust Lane Detection Approach for Autonomous Driving

Autonomous driving has attracted huge research interest from both academia and industry. As one of the key components for the safe driving of autonomous vehicles, lane detection allows vehicles to correctly locate itself in the lane and follow the traffic rules. Unlike traditional detection methods that rely on the extraction of professional and hand-designed features, this paper proposes a robust lane detection method by mining semantic information via the deep learning model LaneNet, which can cope with more complex road scenes, and relieve the restriction of deep learning models that detect a fixed number of lanes. Specifically, we first utilize the LaneNet to segment lane pixels from the road scene. Then we distinguish different lane instances by using a clustering loss function based on the distance vector of lane pixels. Finally, we verify our method on two datasets, Tusimple and CULane. The results show that the detection accuracy of Tusimple is up to 94.3%, CULane’s normal level is 90.4% and other more complex levels can reach up to 70%. Furthermore, by comparing with existing approaches, simulation results confirm the robustness of the proposed method in lane detection. In addition, we combine lane detection with driving decision based on intelligent driving simulation platform PanoSim5, which illustrates the effectiveness of our proposed lane detection method for autonomous driving.
A Real-Time IoT and Image Processing based Weeds Classification System for Selective Herbicide

Nowadays, the Internet of things (IoT) plays a vital role in various sectors, including smart cities, health care, industries, and agriculture. With the advancement of IoT and machine vision-based technologies, such a manual system can be replaced with an automated weed control system. The main objective of this research work is to develop an automatic IoT-based system that detects and identifies weeds with much high accuracy and with a low computational time. Furthermore, we proposed two simple, fast, and effective methods to discriminate broad, narrow, and little weeds. The primary building blocks of the proposed system consist of a pre-processing system followed by Circular Mean Intensities and Discrete Fourier Transform, which are applied to extract useful features from images. A threshold value is then set based on these features to distinguish different classes of weeds. The experimental results produced by the proposed algorithms achieved a classification rate of 96% using 350 images.
Interaction Intention Recognition via Human Emotion for Human-Robot Natural Interaction

In many social scenarios, human emotion governs external behavior, and behavior reflects intention. If a social robot can recognize users’ interaction intention through observable behaviors, it can respond in a personalized way, thus exhibiting "natural" behavior. In this paper, we propose a new method for social human-robot interaction, where the robot obtains the facial expression and body action of the people only from RGB videos to recognize whether humans intend to interact with it or not. Through feature extraction and fusion, a computational model of emotion is established to further identify the interlocutors’ intention. We use a stacking model with 3 basic classifiers to fuse features and create a real unconstrained dataset to evaluate our method. The result shows that our method which combines facial expression and body action features, is more efficient than single-cue based methods with respect to increased accuracy, robustness, and reduced uncertainty.
A Wireless Network of 8.8-mm3 Bio-Implants Featuring Adaptive Magnetoelectric Power and Multi-Access Bidirectional Telemetry

This paper presents a hardware platform for wireless mm-sized bio-implant networks, exploiting adaptive magnetoelectric power transfer and novel schemes for efficient bidirectional multi-access communication. The closed-loop power control mitigates power delivery fluctuations caused by distance and alignment change and avoids redundant power of the external transceiver. The system also enables simultaneous power and time-domain modulated downlink data with a 5% peak power transfer efficiency and a 62.3-kbps maximum data rate at 340-kHz carrier frequency; multi-access uplink of all the implants enabled by individually programmed IF with a 40-kbps maximum data rate at 31-MHz carrier frequency; and more than 6-cm distance between the implant and the external TRX.
A Disturbance Estimation Approach to Self-Calibration of Gimbal Resolver- to-Digital Conversion System

High-precision position measurement of the gimbal servo system is indispensable to accurate torque output of the control moment gyroscope (CMG). This article addresses the systematic error of the resolver caused by the nonideal output signals of the resolver in the gimbal resolver-to-digital (R/D) conversion system. Due to the limitation of the structure and installation space of the CMG, it is impractical to install additional high-precision sensors to calibrate the raw angular position. Thus, this article proposes a new self-calibration method based on the discrete extended state observer (DESO) to compensate the systematic error of the resolver. The systematic error of the resolver is observed through iterating the resolver systematic estimation error and the compensation table is generated by the output of the DESO. Then, the steady-state error and dynamic performance of the DESO is analyzed and simulated. Finally, the proposed self-calibration method is verified by experiments, which show that the angular position control accuracy is significantly improved, which enhances the speed precision of the gimbal servo system.
Multi-round Data Poisoning Attack and Defense against Truth Discovery in Crowdsensing Systems

Crowdsensing systems collect various types of data based on the personal sensing devices of ordinary users. These users are generally the workers of crowdsourcing tasks released by the systems. However, due to the lack of strict authentication for user identity in many crowdsensing services, malicious attackers can sneak into normal workers and submit malicious data to the system to launch data poisoning attacks. Truth discovery algorithms aim to calculate workers' trustworthiness and try to find the ground truth from inconsistent data submitted by different workers. It can help the systems filter out some data providers with poor quality, and thus can defend against some simple data poisoning attacks, such as random attacks, max-value attacks, etc. However, we found that in multi-rounds of data collaction tasks, the attackers can still attack successfully by slightly modifying the simple poisoning strategies. Attackers can deceive the truth discovery algorithm by alternately submitting real and fake data, thereby mislead the system to infer the wrong truth. Therefore, in this paper we study the attack and defense methods for multi-round data poisoning against truth discovery in crowdsensing systems. First, we verify the vulnerability of a class of commonly used truth discovery framework under a multi-rounds of data poisoning strategy named “Hide-AttackPois”. Experiments show that only using a simple hide attack strategy can cause great disturbance to the output of the algorithm. Second, we optimize this class of truth discovery framework to enhance its robustness and enable it to defend against “Hide-AttackPois”. Then, we further improve the data poisoning attack model, so that the model can learn better attack strategies and effectively attack the optimized truth discovery framework. We conduct experiments on real dataset to verify the effectiveness of the proposed method.
Building Trust for Postoperative Pain Estimation: Towards Explainable Machine-Learning Prediction Based on Multimodal Indicators

Postoperative pain estimation in non-verbal populations is clinically challenging due to their invalid self-reports. Existing pain estimation methods ignore the explainability and clinical utility of models, which brings a barrier to model trust. In this paper, we propose an explainable machine-learning prediction for postoperative pain. The proposed method is based on multimodal indicators with the following improvements: (1) we construct a well-benchmarked postoperative pain dataset, and multimodal indicator fusion comprising of both multiscale behavioral features from facial images and general vital signs from physiological measures is constructed; (2) Our method employs the deep stacked network (DSN) to estimate the postoperative pain, and it considers the shared feature mapping to speed up the training speed; (3) We improve the explainability and clinical utility of models through appending a SHapley Additive exPlanations (SHAP) module and a decision curve analysis (DCA) to the framework. Extensive experiments demonstrate the accuracy and efficacy of our method.