Miracle-dz/UniFAR-FTUs huggingface dataset:
Pre-Train-Based Neural-Learning Control of a Novel Parallel Robot by Local Model Approximation

In this work, a design of novel 4-DOF parallel robot is first introduced. Compared to the existing parallel robots, the advantages of this robot are on large translational workspace, large driving force, which extremely suitable for pick and place operating scenario. However, due to the complicated closed-chain structure of the parallel robot, the vital dynamic control solution for this scenario is restricted by the fact that the dynamic model can not meet the real-time calculation requirements. In order to introduce the prior knowledge of robot dynamics into the control system, an online learning control of neural networks based on off-line pre-training by the local dynamic model is proposed which has high computational efficiency and strong robustness. The effectiveness of the proposed control algorithm is verified by two simulation scenarios.
PhysioVec: A Multi-stage Deep-Learning Framework for Searching Online Health Information with Breath Sound

The COVID-19 outbreak presents a major challenge in diagnosing and monitoring respiratory diseases. IoT has the potential to address the challenges by remotely providing patients with rich information about respiratory health. However, current IoT-based health monitoring systems do not provide users with sufficient information to access the rich information in Health Social Network (HSN). We developed PhysioVec, a framework for searching HSN using breath sounds. PhysioVec consists of three components: Local Recurrent Transformer (LRT), a Multivariate radial-basis Logistic Interpreter (MLI), and an existing sentence embedding module. LRT combines local attention and recurrent Transformer to reduce overfitting and improve performance in the segmentation of breathing sounds. Physiological information detected from breathing sounds is used to search for relevant health information. PhysioVec achieved 100%. , 59.8%. , 92.2%. , and 100% precision in the top one search results for breath sound with the common cold, influenza, pneumonia, and bronchitis, respectively. Our proposed framework allows users to search HSN for useful information just by recording their breathing sounds on mobile phones.
An Improved Method of 2D Velocity Estimation of Ship Targets for Spaceborne SAR

It has gradually become a hot spot in the ocean field to monitor ship targets by utilizing spaceborne SAR images. And the velocity estimation of them has become a necessary condition, which can not only obtain the velocity information, but also realize the refocusing and repositioning of the target. In this paper, corresponding improved algorithms are proposed for azimuth and range velocity estimation, which greatly raise the accuracy. And the algorithms are verified by simulation and measured data.
Task Scheduling with Collaborative Computing of MEC System Based on Federated Learning

In response to the ever-increasing demands of users for delay-sensitive applications, issues on shortening the task completion time in the mobile edge computing (MEC) system has aroused widespread concern. From the perspective of task execution order, this work provide a task scheduling scheme for multiple edge nodes (EN) while federated learning (FL) is utilized for the collaboration of the ENs in the MEC system. First, to acquire an efficient execution order for the pending computational tasks that dynamically generated on one edge node, a task scheduling algorithm based on deep Q network (DQN) is proposed, which reduces the average task completion time. Then, based on the federated learning, of which characteristic matches the edge system well, we integrate an aggregation mechanism to take advantage of every participating edge node to obtain a set of global parameter that optimizes task completion delay for all nodes in the MEC system. Simulations verify the effectiveness and the superiority of the proposed algorithm in processing delay-sensitive tasks and analyze the key factors that contributes to the system performance.
Load Frequency Control of Power System Using Honey Badger Algorithm

This paper proposes, a Proportional + Integral + Derivative (PID) controller which is optimally tuned through a new optimization technique known as Honey Badger Algorithm (HBA) for Load Frequency Control (LFC) of single-source Single Area Power System (SAPS). This SAPS comprises a non-reheated turbine thermal power plant as a single generating source with a PID controller for the FLC purpose. For reference, HBA optimized controller is then compared and studied with other four different optimization techniques namely: Particle spawn optimization (PSO), Grey wolf optimization (GWO), Ant lion optimization (ALO), and Moth flame optimization (MFO). Time Response Analysis is performed for all the optimization techniques and important parameters such as Settling Time (ST), Peak Undershoot (PU), Peak Overshoot (PO), and Integral of Time-weighted Absolute Error (ITAE) are studied under 1% of load disturbance in the system. ITAE is taken as an objective function and PID controller gain (Kp, Ki & Kd) are optimizing parameters for the optimization. The result shows better HBA performance over other optimization techniques used for FLC for a single area power system.
Multiuser Scheduling with Enhanced Greedy Techniques for Multicell and Cell-Free Massive MIMO Systems

In this work, we investigate the sum-rate performance of multicell and cell-free massive multi-input multi-output (MIMO) systems using linear precoding and multiuser scheduling algorithms. We consider a network-centric clustering approach to reduce the computational cost of the techniques applied to cell-free systems. We then develop a greedy algorithm that considers multiple candidates for the subset of users to be scheduled and that approaches the performance of the optimal exhaustive search. We assess the proposed and existing scheduling algorithms in both multicell and cell-free networks with the same coverage area. Numerical results illustrate the sum-rate performance of the proposed scheduling algorithm against existing approaches.
Semantic Segmentation of AIS Trajectories for Detecting Complete Fishing Activities

Detection of fishing activities in trajectory data is important for authorities to develop fishery management policies and combat illegal, unreported, and unregulated (IUU) fishing at sea. However, the complex movement patterns of fishing activities challenge existing trajectory segmentation approaches, which may not identify complete fishing activities. In light of this, we propose a window-based trajectory segmentation algorithm which aims to detect fishing activities as completely as possible. Firstly, we introduce a visualization-based technique TPoSTE to help design features characterizing different movement patterns. Secondly, a window-based segmentation algorithm WBS-RLE is proposed to split a trajectory into fishing and non-fishing segments. WBS-RLE first utilizes a pre-trained classifier to label windows in a trajectory as fishing or non-fishing, then it uses the run-length encoding technique to merge those labeled windows into complete fishing activities. The effectiveness of our approach and its advantages over existing approaches are evaluated on a real-world trajectory dataset.
Physical Intelligence in the Metaverse: Mixed Reality Scale Models for Twistronics and Atomic Force Microscopy

Physical intelligence (PI) is an emerging research field using new multi-functional smart materials in mechatronic designs. On the microscopic scale, PI principles give rise to unconventional transducers, which are especially useful for micro/nano-robot design with size and resource constrains. Since it is not easy to directly observe nanoscale multi-physics phenomenon, understanding their principles can be challenging. In this work, we bring PI principles into the metaverse to bridge this gap by developing two mixed reality scale models. The first example is a virtual reality (VR) 2D material twistronics visualizer to demonstrate the novel intelligent 2D materials with tunable properties as a rising field in condensed matter physics. Users can interactively control the cross-coupling multi-physics phenomena and observe the visualized material responses. The second example is centered around an Atomic Force Microscope (AFM) to illustrate its imaging and probe principles. For interaction, users can control the twist angle using atomic lattice models and feel the AFM cantilever force using custom haptic devices. We believe these tools can help precision mechatronic engineers understand and make better use of physical intelligence building blocks to design micro-electromechanical systems.
Feedback Methods to Adjust Finger Orientation for High Accuracy Softness Evaluation with a Wearable Pressure Distribution Sensor in Cervix Examination

Evaluating the softness of the cervix is required during obstetric and gynecological internal examinations to predict the risk of miscarriage and premature birth. In a previous study, we previously developed a wearable pressure distribution sensor to measure the softness of the cervix during an internal examination. However, the sensing operation required precise control to achieve high accuracy. Although wearable sensors are sufficiently flexible to reflect manual operation by humans, they cannot maintain high sensitivity. In particular, the appropriate operation is difficult during internal examination because the cervix is not visible to the clinician. Thus, we attempt to solve this problem by providing feedback on the operation to adjust the sensing operation. In this study, we focused on finger orientation and investigated effective feedback for operational adjustment and softness evaluation. First, we compared the two feedback methodologies using visual and tactile stimuli. The comparison results showed that the perceptual substitute visual method was significantly faster and more accurate in adjusting the orientation. Based on these results, we conducted an experiment to simulate palpation with perceptual substitute visual feedback to verify the effectiveness of the softness evaluation. The results showed that perceptual substitute visual feedback could improve sensor sensitivity and achieve a high-accuracy softness evaluation.
Adaptive Discontinuous Reception in 5G Advanced for Extended Reality Applications

Extended Reality (XR) applications introduce challenging requirements for radio mobile systems in terms of capacity and latency. At the same time, XR devices needs to minimize power consumption to extend battery lifetime and limit dissipated heat. Addressing capacity and latency requirements without excessively increasing power consumption of user devices requires the evolution of 5G power saving schemes. In this work, we propose an Adaptive DRX (ADRX) scheme and design a control policy to optimally adjust active duration of the DRX cycle in order to satisfy XR requirements and minimize energy consumption. To this end, we formulate the problem of selecting the user active time as a stochastic online optimization problem and present a control policy to optimally solve it. Numerical results confirm that the proposed solution increases the number of satisfied users and reduce power consumption compared to standard DRX mechanism. In particular, in a Dense Urban scenario where standard DRX cannot fulfill the requirements of any XR user, ADRX can serve up to 4-5 users per cell with 10% power saving gain.
A Compact Single Transformer Footprint Hybrid Current-Voltage Digital Doherty Power Amplifier

This paper presents a fully integrated single footprint hybrid current-voltage mode digital Doherty power amplifier (PA). A prototype PA is implemented in a 45nm CMOS SOI process. The proposed PA design provides enhanced linearity through adaptive biasing-based AM-PM distortion mitigation of the current mode digital PA and AM-PM cancelation through hybrid current/voltage mode Doherty-based power combining. It achieves 21.7dBm peak output power $(\mathrm{P}_{\text{sat}})$ at 1.2GHz and 37.6% drain efficiency (DE) at 1.4GHz. The proposed digital Doherty PA demonstrates $1.2\times/1.22\times \text{PBO}$ efficiency enhancement, compared to the ideal class-B at 3/6 dB PBO at 1.2GHz. The measured error vector magnitude (EVM) of 64-QAM/20MHz is −23dB with 22.8% average DE without DPD. This is the first demonstration of hybrid current-voltage mode Doherty power combining on a single footprint transformer over a broad bandwidth (BW)
Hydrogen Gas On Carbon-Doped Boron Nitride Nanoribbon Performance Analysis

The variety of physical and electrical properties of electronics devices utilising advanced material can be seen from the energy band structure. This paper investigates Carbon-doped Boron Nitride Nanoribbon (BC2NNR) energy band structure through computational simulation. This is to see how the energy band gap of BC2NNR varies when the width, n is changing. Subsequently, hydrogen gas is attached at three different positions on BC2NNR and the shifting of the sub-bands is observed in the energy band structure. For both pristine BC2NNR, and hydrogen-BC2NNR cases, the density of state (DOS), as well adsorption energy and charge transfer are evaluated to study the sensing performances. From the simulation, a notable reduction in the energy band gap by over 50% is found for pristine BC2NNR with width, n=4 compared to BNNR. The three different positions of hydrogen show significant changes in the charge transfer and infinitesimal changes in the adsorption energy, indicating there is no favourable binding site for H2 gas on BC2NNR. The result presented here provides compelling evidence for using BC2NNR for gas sensing applications.
C3Net: A Cross-Channel Cross-Scale and Cross-Stage Network for Single Image Super-Resolution

In this paper, we propose a cross-channel, cross-scale, and cross-stage network (C3Net) for single image super-resolution, which effectively shares the features learned from multiple channels, multiple scales, and multiple stages. Multi-scale spatial features are extracted in each stage in an encoder-decoder fashion. The channel attention is performed after each encoder to exploit the inter-channel dependencies. After that, we design a cross-stage and cross-scale feature sharing module to accelerate the feature sharing across different scales and different stages. The whole network is optimized by multiple similar stages to reduce the number of parameters. Finally, super-resolution images of multiple resolutions are reconstructed simultaneously. We evaluate the proposed network on four benchmark datasets by comparing it with eleven state-of-the-art methods. Comprehensive experiments show the proposed network outperforms state-of-the-art methods by fewer parameters. The source code is available at https://github.com/thinkerww/SR_Version.
22–33 GHz CMOS LNA Using Coupled-TL Feedback and Body Self-Forward-Bias for 28 GHz 5G System

We report a 22–33 GHz low-noise amplifier (LNA) with low power dissipation (PD), low noise-figure (NF) and small group-delay (GD) variation in 90 nm CMOS for 28 GHz 5G system. Current-reused and body self-forward-bias (BSFB) techniques are used for low PD. Compact quarter-wavelength (λ/4) spiral transmission-line (TL) in conjunction with a large bypass capacitor is used for gate-bias and simultaneous drain-bias and current-source. Coupled-TL feedback and BSFB techniques are used for gain and NF enhancement in the condition of the same PD. The LNA dissipates 12.2 mW and achieves decent S21 of 16 dB, 3 dB bandwidth (f3dB) of 11 GHz (22–33 GHz), minimum NF (NFmin) of 2.5 dB, average NF (NFavg) of 3.1 dB and GD variation of ±6 ps for 22–33 GHz, and figure-of-merit (FOM) of 91.7 GHz. Furthermore, the LNA occupies 0.406 mm2 chip area, and attains decent input third-order intercept point (IIP3) of -3 dBm at 28 GHz. The NF and FOM are one of the best performances ever demonstrated for Ka-band CMOS LNAs with f3dB wider than 5 GHz and PD lower than 14 mW.
Prediction of COVID-19 Through Chest X-Ray Images Employing Various Machine Learning Techniques

The COVID-19 is the most infectious disease which is recently discovered. The COVID-19 pandemic has led to excruciating loss to human life and it also caused mild to severe respiratory illness, including death. Detecting the infected patients and taking special care is crucial in fighting covid-19. Radiography and Radiology images are used to diagnose the patients. These are the fastest ways to identify COVID-19 disease. It is observed that patients affected with COVID-19 have specific abnormalities in their chest radiograms. Initially there were few limited set of CT images are available publicly in performing research. Board-certified radiologist role is to perform identification of images exhibiting COVID-19 disease. Chest CT scans are helpful to diagnose COVID-19 disease in individuals. COVID-19 directly shows impact on lungs and it damages and the tiny air sacs. In this paper we have used deep transfer learning models Residual Network (ResNet50) and VGG19 (Visual Geometry Group) to predict the disease at earlier stages. These models obtained a specificity rate of 90% and achieved a highest sensitivity rate of 98 %. In addition to sensitivity and specificity rates ROC curve, average prediction and confusion matrix of each model are presented in the papers. While this achieved performance is very encouraging if we have large set of COVID-19 images then it may give more reliable estimation of accuracy rates.
UWB Microwave Photonic Noise MIMO Radar for Through-Wall Ranging and Imaging

We propose an ultrawideband (UWB) microwave photonic noise multiple-input-multiple-output (MIMO) radar for through-wall ranging and imaging applications. Combining the noise signal generated by the erbium-doped fiber amplifier (EDFA) and MIMO radar technology, the real-time performance, resolution and anti-interception probability of through-wall radar are significantly improved. High-resolution ranging and imaging of single and multi-targets behind the wall are numerically realized.
Feature Extraction of Sequence Data Based on LSTM and its Application to Fault Diagnosis of Industrial Process

Data with sequential relationships such as text, speech, and sensor data, belong to sequence data. Many deep learning models are proposed to extract features and learn inherent information among the sequence data. We investigate the similarities and differences of the modeling methods of sequence data in various fields, and then use deep learning neural network to build a model for the fault diagnosis of sequence data in process industry. Long-short-term memory (LSTM) network and feature extraction of contrastive predictive coding (CPC) are used in this paper for building deep learning models. CPC can learn representations of data, and LSTM can learn the information contained in sequence data. For application to fault diagnosis, the whole model combining CPC and LSTM can appropriately distinguish normal data from various fault data. Experiments on the Tennessee-Eastman process show that the LSTM model combining with proper feature extraction methods can improve the performance of fault diagnosis.
AUTOMATISE: Multiple Aspect Trajectory Data Mining Tool Library

With the rapid increasing availability of information and popularization of mobility devices, trajectories have become more complex in their form. Trajectory data is now high dimensional, and often associated with heterogeneous sources of semantic data, that are called Multiple Aspect Trajectories. The high dimensionality and heterogeneity of these data makes classification a very challenging task both in term of accuracy and in terms of efficiency. The present demo offers a tool, called AUTOMATISE, to support the user in the classification task of multiple aspect trajectories, specifically for extracting and visualizing the movelets, the parts of the trajectory that better discriminate a class. The AUTOMATISE integrates into a unique platform the fragmented approaches available in the literature for multiple aspects trajectories and, in general, for multidimensional sequence classification into a unique web-based and python library system. We illustrate the architecture and the use of the tool for offering both movelets visualization and a complete configuration of classification experimental settings.
AB-HT: An Ensemble Incremental Learning Algorithm for Network Intrusion Detection Systems

Most machine learning models used in network intrusion detection system (IDS) studies are batch models which require all targeted intrusions to be present in the training data. This approach is slow because computer networks produce massive amounts of data. Furthermore, new network intrusion variants continuously emerge. Retraining the model using these extensive and evolving data takes time and resources. This study proposes AB-HT: an ensemble incremental learning algorithm for IDSs. AB-HT utilizes incremental Adaptive Boosting (AdaBoost) and Hoeffding Tree algorithms. AB-HT model could detect new intrusions without retraining the model using old training data. Thus, it could reduce the computational resources needed to retrain the model while maintaining the model’s performance. We compared it to an AdaBoost-Decision Tree model, a batch learning model, to analyze the effectiveness of the incremental learning approach. Then we compared it to other incremental learning models, the Hoeffding Tree (HT) and Hoeffding Anytime Tree (HATT) models. The experimental results showed that the proposed incremental model had shorter training times than the AdaBoost-Decision Tree model in the long run. Also, on average, the AB-HT model has 18% higher F1-score values than the HT and HATT models. These advantages show that the AB-HT algorithm has promising potential to be used in the IDS field.
Towards Underwater Image Restoration: A Physical-Accurate Pipeline and a Large Scale Full-Reference Benchmark

Underwater images always present low-quality features such as low contrast, blurred edges and color distortion, which brings great challenges to high-level underwater vision tasks. In this paper, a novel underwater image restoration method, namely MonoUIR (Monocular Underwater Image Restoration), is proposed, which is based on a more physical-accurate imaging model compared to existing schemes. And with the monocular depth estimation, MonoUIR has no dependence on extra ranging equipment or specific shooting operations. Experimental results demonstrate that MonoUIR overwhelmingly outperforms other physical model-based competitors. In addition, the Real-world Undersea Color Board (RUCB) dataset is established, providing the illconditioned underwater images collected in the East China Sea and the corresponding high-quality references. To our knowledge, this is the first full-reference underwater benchmark dataset collected entirely in a real-world marine environment, which will further support the fullreference evaluation of underwater image restoration approaches. The source code and the dataset are available at https://TongJiayan.github. io/MonoUIR-Homepage.