Miracle-dz/UniFAR-FTUs huggingface dataset:
Single-objective Distributed Permutation Flowshop Scheduling Optimization via Multi-objectivization by A Helper Objective

Distributed manufacturing has attracted much attention in recent years given the development of globalization and supply chain. As a part of it, distributed permutation flowshop scheduling problem is basic and essential. The problem is often modeled as a constrained single-objective optimization problem, i.e., minimizing the makespan subjected to a number of constraints. Since the problem is challenging to solve, this study proposes to introduce a helper objective, converting the single-objective problem into a multi-objective problem. The helper objective, in principle, is related to, but much easier to solve than the original problem. Through information sharing, the original problem thus can be effectively solved by utilizing the solution of the helper objective. Specifically, an improved swarm intelligence optimization algorithm based on multi-task paradigm is applied to optimize the original and helper objective functions simultaneously. Experiments on a set of test instances contributed to SOA demonstrate the effectiveness of the idea.
Sparse Bayesian Learning Based Tensor Dictionary Learning and Signal Recovery With Application to MIMO Channel Estimation

In this paper, we develop solutions for sparse tensor signal recovery (SR) and tensor dictionary learning (DL) problems via the sparse Bayesian learning (SBL) approach. We consider a class of tensor system which has the special sparsity structure that a given (say the $i$th) row of every unfolding matrices of the tensor involved is simultaneously zero or non-zero. For both problems, we propose a Kronecker-like prior distribution for the variables to be recovered in the framework of SBL to take advantage of this sparsity structure. For tensor DL, we consider a de-noising problem in which the clear version is recoverable from sparse coefficients and several separable dictionaries. Our prior distribution model for sparse coefficients entails that the same column of these separable dictionaries have a common prior distribution. We show that our SBL-based algorithms for solving the SR and DL problems require much lower complexity than that of the corresponding vector-matrix system by reducing the matrix inversion size. The proposed SBL-DL and SBL-SR algorithms are utilized, invoking a tensor virtual channel model, to estimate the channel response of a millimeter wave communication system which employs uniform planar arrays on both sides. The superiority of the resulting channel estimates is verified by computer simulations.
A Coupled Vehicle-Signal Control Method at Signalized Intersections in Mixed Traffic Environment

Signalized intersections play a vital role in addressing the issue of transportation efficiency and vehicle fuel economy in urban areas. Meanwhile, with the development of Connected and Automated Vehicles (CAVs), the mixed traffic environment composed of traffic participants with differing intelligent levels will become an important stage of the intelligent transportation system. Considering the changes in the mixed traffic environment, this paper proposes a Coupled Vehicle-Signal Control (CVSC) method to optimize the traffic signal timing and driving trajectories of CAVs at the same time, with the goals of traffic efficiency improvement and energy saving respectively. The signal timing is continuously optimized to minimize the total delay at the intersection. CAVs generate eco-driving trajectories using the received signal timing information and the planned arrival time to reduce fuel consumption. Finally, simulation experiments were been carried out to verify the control effect of the proposed CVSC. On the one hand, the influence of the involved parameters on the optimization results was analyzed and discussed. On the other hand, the proposed CVSC method was compared with the traditional CACC control and the classic eco-driving model, GlidePath. The research results show that the proposed CVSC method can effectively improve the performance of signalized intersections. When the penetration rate of CAVs is greater than 40%, this method can save fuel consumption by 6%-14% and increase the average speed by 1%-5%.
Center Attention Network for Hyperspectral Image Classification

Classification is one of the most important research topics in hyperspectral image (HSI) analyses and applications. Although convolutional neural networks (CNNs) have been widely introduced into the study of HSI classification with appreciable performance, the misclassification problem of the pixels on the boundary of adjacent land covers is still significant due to the interfering neighboring pixels whose categories are different from the target pixel. To address this challenge, in this article, we propose a center attention network for HSI classification. The proposed method simultaneously captures spectral-spatial features of the target pixel and its neighboring pixels for classification. Specifically, the method adopts a center attention module (CAM) that pays more attention to the features which are more correlated with the target pixel, that is, the central pixel of the sample, and then sums up the weighted features to generate more relevant and discriminative features. In this way, our method has a high potential for improving the performance of HSI classification. In addition, the CAM greatly reduces the number of parameters in the network via weighted sum of the spectral-spatial features, thus improving the computing efficiency while still maintaining classification accuracy. We evaluate the proposed method on three public datasets, and the experimental results demonstrate the superiority of our method on accuracy and efficiency compared with several state-of-the-art methods.
AD diagnosis assistant system based on convolutional network

It has been a hot topic to detect Alzheimer’s disease (AD) from MRI and other neuroimaging data by machine learning in recent years. A large number of deep learning models have been developed, such as alexnet, VGg net, Gan and so on. The common limitation of these models is that they rely on a large number of labeled training images and need deeper network structure to achieve higher accuracy. It is challenging to obtain a large number of labeled data in the field of medical image. Training a suitable medical image classification model from scratch also requires a lot of resources. To solve these problems, in this paper, we try to use convolutional network (CNN) transfer learning for image classification training, select CNN network model, fine tune the network weight layer by layer, modify the last layer of CNN for training on MRI image. In order to reduce the size of training data, we use image entropy to select the most informative slice. Through the experiments on ADNI data sets, compared with other contemporary methods, we achieve 99.89% on the classification of Alzheimer’s disease (AD), Normal Cognitive (NC) and Mild Cognitive Impairment (MCI). Attractively, class activation mapping (CAM) is employed to visualize brain regions related to AD, which further gives the targeted information for AD diagnosis.
A Community Detection Method for Social Network Based on Community Embedding

Most community detection methods focus on the similarities between detection nodes to achieve community partitioning. Traditional network representation learning methods are also limited to the local context of the central nodes, which results in less truly representative results. This article examines nodes’ influence information, nodes’ community affiliating information, and similarity of community topologies and proposes a more effective node representation strategy. According to the local node information and global topology in the social network graph, a method of combining local node embedding and global community embedding is also designed. The effectiveness of learning node representation and community representation is improved by our approach. The proposed model can also effectively detect overlapping communities.
Mechanical Design and Torque Estimation Model of a Variable Stiffness Joint with Hysteresis Characteristics

This paper presents the design of a variable stiffness joint utilizing a lever mechanism for changing the stiffness and its real-time computational model to estimate applied joint torque taking into account the nonlinear hysteresis characteristics. Typical stiffness model considering hysteresis is difficult to be applied on real-time estimation because it depends on the deformation velocity value of which its accurate value couldn't be achieved in general digital measurement meters. However, the proposed computation model employs deformation value being measured easily, thus can be used to estimate the applied force based on the defined pivot position of lever mechanism on a realtime basis. By comparing the experimental and the simulation results, it was shown that the proposed computation model can estimate the torque only with the deformation measurement. Furthermore, by this computation model, it was verified experimentally whether the torque generated in the joint axis by the external force can be estimated correctly in real-time.
Coordination of Spatially Distributed Electric Vehicle Charging for Voltage Rise and Voltage Unbalance Mitigation in Networks with Solar Penetration

This paper proposes a decentralised control strategy using droop-based control to generate a charging schedule for electric vehicles (EV). This strategy aims to mitigate voltage rise caused by rooftop photovoltaic (PV) systems and phase unbalance caused by unbalance loads in a low-voltage distribution (LV) network. The strategy also smooths the voltage profile of the network and reduces variation between the maximum and minimum voltages. A modified IEEE 13 node test feeder with an LV network based on a semi-rural Australian town was used to show the impacts of PV and EV charging in the network and verify the proposed strategy.
3-D Inspection Method for Industrial Product Assembly Based on Single X-Ray Projections

Industrial product assembly inspection is an indispensable part of industrial production, directly affecting product performance and reliability. Due to the capsulation of many products, most of the current inspection methods are based on X-ray technologies, such as digital radiography (DR) and computed tomography (CT). The inspection methods based on DR are fast but cannot obtain the spatial distributions or interrelationships of the assembled components and are easily affected by the occlusion of internal components. The detection methods based on CT can reconstruct the 3-D volume of a product and have high detection accuracy but cannot achieve real-time inspection because of the reconstruction speed. To address the above problems, we propose an inspection method based on a single-projection reconstruction network for industrial product assembly. An improved autoencoder network is trained to learn the shared structural features in 3-D volumes from 2-D projections, realizing the reconstruction of the 3-D volume at a specific viewing angle from a single projection from any viewing angle. The 3-D volumes output by the network are used to detect the position and posture of the components in the product to judge whether the product has been assembled correctly. The feasibility of the proposed method is verified with the data of a certain fuse under different assembly conditions. The experimental results show that the proposed method can improve the inspection efficiency, reduce the inspection costs, and simplify the imaging system.
Research on ZigBee-based Remote Water Temperature Monitoring and Control System

According to the disadvantage of high cost of traditional temperature and humidity monitoring system, cumbersome wiring, time consuming and laborious and poor scalability, this paper presents a remote water temperature monitoring and control system based on ZigBee, which could achieve remotes monitoring of water temperature and solves the acquirement for traditional temperature monitoring systems. The volumn of signal transmission lines is large and relatively high in cost, which are arranged in the monitoring area. We utilize the advantages of ZigBee network security, low power consumption, strong networking ability, etc. to improve the reliability of the remote water temperature control system.
Moving Target Tracking for Single UAV In Open Outdoor Environment

The task of tracking a moving target by the UAV has two major challenges: one is that the multi-angle and multi-scale changes caused by the target motion make the target detection more difficult, and the other is the target loss due to variable direction of movement. Most of the existing research is limited to simulation studies, while the remaining few studies only allow the UAV to track specific target in specific environments. In order to solve these problems, we design and validate a new system framework for UAV tracking moving target in an open outdoor environment. In this framework, we use YOLOv3 to detect the multi-angle and multi-scale moving target, locate the target based on monocular vision, and design a tracking strategy, Mass Spring Algorithm (MSA), to prevent the loss of moving target. The experimental video11https://www.bilibili. com/video/BV1SD4y197gF/ shows that the system framework we designed is effective and successful. The results of comparative experiments to MSA shows that it effectively prevents the loss of target in the tracking process.
The use of technical vision in the robotic cash counting area

In this research we have installed a computer vision system on a single board computer named ODROID™ XU4. This project is a modification of a more complex and bigger system – the robotic cash counting area. Before our modification a trained neural network was installed on a single board computer, but this caused the computer’s CPU (central processing unit) to be overloaded and soft was shutting down. We implemented and compared two methods of object detection in an image and video, namely Shi-Tomasi and FAST (Features from Accelerated Segment) methods. The comparison criteria were accuracy of detection and CPU load.
Microcavity Fiber SERS Probe Coated With Ag Nanoparticles For Detecting Antibiotic in Milk

In this paper, an Ag nanoparticles (AgNPs) modified multimode fiber (MMF) SERS probe prepared based on sol self-assembly method is proposed and fabricated. Different fiber SERS probes (AgNPs-MMF-x, x is the self-assembly time) are obtained by adjusting the self-assembly time. Using rhodamine 6G (R6G) solution as the probe molecule, the far-end performances of the prepared fiber SERS probe are studied, and the AgNPs-MMF-30 probe with obvious advantages in SERS performance is obtained. The limit of detection (LOD) for R6G is as low as 10−9 M, demonstrating the high sensitivity of AgNPs-MMF-30 probe. The enhancement factor (EF) of AgNPs-MMF-30 probe to 10−7 M R6G is about 1.36 × 108. In addition, the AgNPs-MMF-30 probe has an outstanding reproducibility across the entire area with the maximum value of relative standard deviation (RSD) of less than 10.94%. The AgNPs-MMF-30 probe exhibit a long-term stability regarding Raman enhancement of up to 35 days. Importantly, the high-performance AgNPs-MMF-30 probe is further applied as a highly sensitive SERS platform for the trace detection of antibiotic in milk. The fiber SERS probe has the characteristics of fast, effective, remote real-time detection and so on, so it has great potential application in biochemical sensing and food security.
LFNet: Lightweight fire smoke detection for uncertain surveillance environment

Recently, many fire disasters took place all over the world. Therefore, the research communities pay more attention to the algorithms that can accurately detect fire disasters in the monitoring scene. Although the current detector algorithms achieve excellent performances in the benchmark object detection task, they can not achieve high recognition accuracy in fire monitoring tasks due to the fire disaster has no distinct rules in shape, color, and size. Moreover, the traditional learning-based object detection algorithms have limitations in model size and computational cost, so it can not be well applied in resource-constrained devices and Tactile Internet. In terms of the dataset, current learning-based object detection algorithms do not consider the impact of some fair weather. The models trained by such training sets are easily affected by the weather. We propose a new Lightweight model called LFNet, which has a small size and low computational cost to solve these problems. We also published a large-scale benchmark dataset to train the fire disaster detection model. To make the learning-based model trained by the proposed dataset possess higher robustness in various weather conditions, we utilize the atmospheric scattering model to simulate the original images into pictures affected by different degrees of simulated haze and dust for training. Experimental results show that the proposed LFNet can achieve state-of-the-art performance with limited model size. The presented data augmentation method can significantly improve the robustness of the current object detection model.
Performance Enhancement of SCM/WDM-RoF-XGPON System for Bidirectional Transmission With Square Root Module

The 10-Gigabit Passive Optical Network (GPON), also known as (XGPON), is a key technology for the next generation of optical fibre communication systems that can improve reliability data rate. However, the increment in the transmission distance and data rate will lead to increased dispersion. This paper proposes a model based on the Sub-Carrier Multiplexing/Wavelength Division Multiplexing-Radio over Fibre-10-Gigabit Passive Optical Network system (SCM/WDM-RoF-XGPON) for Radio Frequency (RF) using a conventional Optical Network Unit (ONU) with the Square Root Module (SRM) on the side of the receiver. It is connected to a central office Optical Line Terminal (OLT) via an access network for multi-channel optical fibre transfers with a distance of 80 km and at 10 Gbps data rate. The Optisystem 15 simulation software is used to evaluate the proposed system based on the Bit Error Rate (BER), Quality factor (Q-factor) and eye diagram. The proposed system of bidirectional fibre links uses 1270 nm and 1577 nm wavelengths for uplink and downlink transmissions, respectively. The performance is further enhanced by using SRM to compensate for the square law characteristics, making it suitable for broadband services. Due to the utilisation of SRM in the architecture, the reported results reveal a double enhancement in successful transition performance at an optical fibre length of 80 km distance. The BER displayed significant improvement (less than 1.00E-09) with SRM at 80 km; however, without SRM and at 50 km, the BER is less than 1.00E-09. Investigations have presented an enhancement in the Q-factor’s effectiveness with the use of SRM, which helps to increase the XGPON length. When the length of the fibre is 80 km, the Q-factor is 6 with SRM, while it is 3.7 without SRM.
Co-Optimization of Morphology and Actuation Parameters of Multi-Sectional FREEs for Trajectory Matching

Fiber Reinforced Elastomeric Enclosures (FREEs) have gained significant popularity as a form of soft artificial muscle for the diversified deformation behaviors upon pressurization. In particular, modular FREEs connected in series, demonstrate enhanced flexibility and reconfigurability, thus are adaptable to more complex trajectory tasks. Morphology and motion parameters are strongly coupled for soft manipulators to achieve desired motion behaviors. In contrast to the traditional way of alternating between design optimization in either morphology or actuation space, we seek to co-optimize over parameters in both spaces in an end-to-end fashion with gradient-free optimization. The co-optimization framework allows for faster convergence towards optimal performance given desired trajectory plans. We demonstrated the feasibility of the framework with two simulated tasks: the multiple shape matching task and the octopus reaching task. The former task was further evaluated with real-world experiments.
Adaptive Speech Enhancement Algorithm Based on First-order Differential Microphone Array

To solve the problem of directional interference noise in portable voice communication equipment, a dual-channel adaptive speech enhancement algorithm combined with the first-order differential microphone array and variable-step-size frequency-domain Least Mean Square (LMS) algorithm is proposed. First, the speech channel signal and noise channel signal were obtained by using the first-order differential array process the noise signal by microphone. Then, the residual noise in the speech channel signal was further eliminate by using the two channel signals combined with the variable-step-size frequency-domain LMS to estimate the optimal step length to control the update of the frequency-domain weight coefficient of the adaptive filter, thereby improve speech quality and intelligibility. The simulation results show that the algorithm overcomes the influence of directional noise effectively, and the objective evaluation of speech quality and the effect of noise reduction are better than the comparison algorithm.
E-Learning Research in The Middle East: A Bibliometric Analysis

E-learning published research articles has been increasing consistently. Such an abundance availability of knowledge warrants for investigation looking into bibliometric characteristics of a specific research area. The objective of this study is to present the research trend of e-learning in Bahrain, Saudi Arabia, United Arab Emirates, Oman and Kuwait. This study adopted a bibliometric analysis based on 430 documents obtained from the Scopus database. To achieve the objectives of this study, various tools have been employed, such as Harzing's Publish or Perish, VOSviewer, and Microsoft Excel. This study presents the results using standard bibliometric indicators. Finally, this study extends findings on e-learning literature by using bibliometric approach and provide meaningful insights on the trend of previous literature.
A Robust LiDAR State Estimation and Map Building Approach for Urban Road

In a dynamic environment, the long-term and reliable positioning of the robot system is the key. Good automatic driving needs to solve the influence of multiple dynamic objects in the road environment, which puts forward higher requirements for the location accuracy and the robustness of the self-driving vehicle. In this article, we mainly research the positioning problem in the urban road environment, and we design a lidar location and navigation system based on multi-sensor fusion. First, the low-cost VLP-16 lidar is used as the front end of the system to obtain the surrounding high-precision point cloud, and the inertial measurement unit (IMU) is used to replace the uniform motion model to remove the movement distortion of the lidar. The optimized point cloud information constructs a local map. After that, we combined low-cost GPS information to optimize the global map accuracy by using map optimization. Finally, the local map saved on the hard disk is generated through our optimization method to generate a global laser point cloud map of any size. At the same time, we found in actual tests that a major factor that affects the positioning accuracy of lidar on urban roads is the dynamic and semi-static moving vehicles on the road. In order to not be affected by the occlusion of parallel vehicles on the front and rear of the road when building the map, the deep point cloud neural network is used. The network recognizes these vehicles, and extracts point clouds through multi-target tracking, and removes the corresponding point clouds in the local and global maps. Autonomous driving vehicles in the park that meet the above test requirements are tested. The experimental results show that the multi-sensor fusion and dynamic target removal positioning system we designed meets the requirements of urban road positioning under multiple dynamic targets.