Miracle-dz/UniFAR-FTUs huggingface dataset:
Trajectory Tracking Control for Autonomous Parking Based on Adaptive Reduced-horizon Model Predictive Control

In this paper, an adaptive reduced-horizon model predictive control is proposed for autonomous parking trajectory tracking. Given the reference trajectory, the discrete linear time varying model is obtained by linearizing and discretizing along the reference trajectory point. Furthermore, the model is reformulated into a combined incremental form. Then, a standard quadratic programming problem is established, and the optimal control strategy is obtained by solving the problem online at every time instant. Meanwhiles, the prediction horizon will reduce adaptively by solving the constrained optimization problem, and it will minimize the computation time complexity of the MPC-based controller. The actual parking scenarios are co-simulated in Simulink and Carsim, which shows the effectiveness and feasibility of the proposed method.
Performance Evaluation of Synthetic Aperture Radar Images

Synthetic Aperture Radar (SAR) imaginary is used extensively for Military applications in the modern era of technology. In this work, we have evaluated the performance of SAR images for Radar Systems. The SAR length, Resolution, and Footprint of SAR networks have been compared by varying the parameters of Radar signals like Bandwidth, Operating Frequency, and Satellite Altitude. From the results, it was observed that Antenna Footprints vary inversely with the Operating Frequency and Grazing Angle of Radar
Settling Time of Current-Tunable Probabilistic Bit's Distribution

Probabilistic bits (p-bits) based on magnetic tunnel junctions are of recent interest in probabilistic and neuromorphic computing architectures based on their small size, high operating speeds, and truly stochastic nature. In practical systems, the output probability of the bit can be tuned by an applied current, which is generally characterized by a quasi-static tuning curve. In this letter, we instead focus on the finite time it takes the p-bit's probabilistic distribution to respond to an applied bias current. We find that this settling time is in the range of hundreds of picoseconds for a typical junction, and is highly dependent on various parameters, including the device size, material properties, and magnitude of the applied current. These results provide a baseline understanding of the dynamic properties of a nanomagnetic p-bit's probability distribution, which is helpful for p-bit-related system architecture discussions.
Deep Learning System for Defect Classification of Solar Panel Cells

Solar photovoltaic technology can be regarded as a safe energy generation system with relatively less pollution, noiseless, and abundant solar source. The operation and maintenance costs for solar panels are almost negligible as compared to costs of other renewable energy systems. However, due to the exposure to different weather conditions like extreme heat, humidity, dust storms and rain, the panel modules are liable to kind of defects which lead to power degradation, shading, bridging, power loss and fire hazard. Visual inspection of solar panel cells by experts is stressful, time consuming and unreliable. The Electroluminescence (EL) method with the use of infra-red cameras make deep learning algorithms promising in solving the problem. In this paper, we applied several deep learning networks such as AlexNet, SENet, ResNet18, ResNet34, ResNet50, ResNet101, ResNet152, GoogleNet (Inception V1), Xception, Vision Transformer (Vit), YOLOv3, and SqueezeNet to classify solar PV cell defects. We applied the models on the 2,624 elpv benchmark images using both binary and four classifications. But due to limited defect classifications with elpv benchmark dataset, we extracted EL images from publicly available datasets of a total of 18,347 Photovoltaic (PV) cells images with 11 types of defects in addition to the non-defective PV cells. We compared the results of the elpv benchmark with the extracted elpv images using accuracy, F1 score, precision and recall metrics. The results show that the xception model has the highest accuracy from 56.296% on elpv benchmark to 91.399% on the extracted elpv datasets.
Path Tracking Dynamics Control for All-Wheel Steering Unmanned Ground Vehicle and Experimental Validation

In this paper, a lateral dynamics controller for path tracking is proposed for all-wheel steering UGV. The controller is based on feedforward-feedback control strategy and zero sideslip angle strategy to improve both the path tracking and steering stability performance. The stability analysis of the closed-looped error dynamics is conducted based on pole location analysis method, which show that the controller remains stable against parameters uncertainties in most conditions except for rear tire saturation. Finally, a all-wheel steering UGV is used to validate the proposed controller. The autonomous driving experiments prove that the proposed controller successfully achieves the designed control goal, while improving both the path tracking and steering stability performance.
Resource Allocation for NOMA Based OTFS Transmission with Heterogeneous Mobility Users

This paper investigates the reliability performance of the non-orthogonal multiple access (NOMA) based orthog-onal time frequency space (OTFS) system, namely NOMA-OTFS, where both high-mobility and low-mobility users coexist. To minimize the average bit error rate (BER), we formu-late the resource allocation for the downlink NOMA-OTFS transmission, where the user pairing, transmit power, and resource blocks are jointly optimized. To solve this mixed integer programming problem, we first develop the power allocation scheme in the scenario of only two users, then apply it to the general case and obtain the scheme of user pairing and resource block allocation by a proposed near-optimal NOMA users and resource blocks three-sided matching alogrithm (URMA). The simulation results demonstrate that the proposed algorithm significantly outperforms the random matching method and greedy algorithm, and its performance is very close to the optimal exhaustive method.
EEG Emotion Recognition using Multisource Instance Transfer Learning Framework

In recent years, EEG has been widely used in the field of emotion recognition. However, the instability of EEG signals leads to great differences not only between different subjects, but also the same subjects in different sessions, so the same emotion recognition models cannot be shared with each other, resulting in new participants need to collect new label data to train new models. This is time-consuming and labor-intensive. In order to solve this problem, we propose a multi-source instance transfer learning framework based on ensemble learning, which uses the instance-transfer method to reduce the dependence on the target domain labeled data, and combines ensemble learning to improve the accuracy of emotion recognition. The framework first filters the source domain data using a base classifier trained on existing label data. The filtered multi-source domain data is then combined with the existing target domain labeled data to train the corresponding classifier. Finally, all classifiers are integrated together for a test experiment on the unlabeled data of the target domain. Our method has been experimented on the SEED dataset and the SEED-IV dataset, and the classification accuracy of the experimental results has been significantly improved compared with the base classifier, and the cross-subject and cross-session scenarios have been significantly improved. Among them, the cross-session increased (5%-10%), and the cross-subject increased (2%-3%). This is a simple, fast, and flexible framework that has experimentally demonstrated the transfer learning framework’s effectiveness for multi-source EEG emotion recognition (different emotion can achieve different brain signals) problems.
First results from the Goddard High-Resolution Spectrograph - The Galactic halo and the Ly-alpha forest at low redshift in 3C 273

Results of initial spectroscopy using the Goddard High-Resolution Spectrograph and the Hubble Space Telescope on the bright low-redshift quasar 3C 273 are presented. Absorption lines produced by gas in the disk and halo of the Galaxy from numerous neutral and weakly ionized atoms and also from highly ionized atoms are detected. The detection of N v provides additional strong support for the existence of gas with T about 200,000 K at large distances away from the disk of the Galaxy. Ten absorption lines with EW greater than 50 mA, and four possible lines with EW between 25 and 50 mA, are also found in the region between 1235 and 1406 A. These cannot be identified with any lines in the galactic interstellar medium and must therefore all be Ly-alpha systems at very low redshifts. This number of lines is 5-10 times larger than expected from a simple extrapolation of the high-redshift evolution derived from ground-based observations and is consistent with no evolution in cloud properties since z = 2. 28 refs.
Laser Actuation of 3D-Printed Microbeam with CNT as Functional Layer

We present a novel method to set in motion a 3D-printed microbeam using IR laser diode radiation. Thanks to this an intentional actuation, so-called 4D-printing, was achieved. As a smart material, for local absorption and conversion of IR radiation energy into heat, a carbon nanotube coating is used on the top microbeam surface. To the best of the authors’ knowledge, this type of approach has not been presented in the literature so far and it offers a technological novelty in the field of 3D printed microactuators for MEMS.
Design and Development of a Patient-specific surgical guide for free fibula flap surgery

Ahstract- A conventional approach for mandibular reconstruction is the fibular free flap. The surgical procedure's accuracy, on the other hand, is largely determined by each surgeon's experience. Extra bone loss, longer operative times, and excessive blood loss are only a few of the major concerns with the surgery. We present a step-by-step design of a patient-specific 3D printed surgical guide in this paper, which will aid in effective clinical outcomes by improving surgical accuracy, providing exact cuts, decreasing fixation complications, and reducing the operative time. The DICOM information of a male patient with a left mandibular tumor were used in this study. The surgical guides and models for the mandible and fibula were developed using free CAD/CAM software and successfully 3-D printed on an FDM printer. We were able to build surgical guides and undertake virtual simulations of mandibular repair utilizing fibular bone fragments before the actual surgery, which have given high reconstructive accuracy. The guided surgery was carried out successfully. The guides fit on both the mandible and the fibula were determined to be extremely precise and accurate. The use of 3-D printed surgical guides helped to ease the mandibular reconstruction procedure.
Siamese-PointNet++: Point Cloud Classification with Siamese PointNet++

It is significant to explore the related information of point pairs to improve the classification accuracy of a point cloud. This paper proposes the Siamese PointNet++, which is end-to-end trained offline with point-set pair. More specifically, PointNet++is used to extract features from point-set pairs, and then a relation module with 1D CNN architecture is applied to compute the relation scores. We conducted extensive experiments on the test data from the 3D point cloud classification challenge of the 2019 IEEE GRSS Data Fusion Contest. The inspiring experimental results demonstrate the effectiveness of the proposed framework.
Subpicosecond kilowatt pulses from a mode‐locked cw dye laser

Subpicosecond pulses (0.5–1.0 psec) have been generated by passive mode locking of a composite medium dye laser. Both the gain medium and saturable absorber are placed in a single free‐flowing dye stream located near the center of the resonator. Peak pulse powers of several kilowatts have been obtained by acousto‐optically dumping the mode‐locked pulses from the optical cavity at repetition rates up to 105 pps.
MRI and CT Image Fusion using Synchronized Anisotropic Diffusion Equation with DT-CWT Decomposition

Medical image fusion plays the major role in many applications including brain tumor segmentation, and classification. But the conventional methods are suffering with colour artifacts. Thus, this article proposes a novel magnetic resonance imaging (MRI) and computerized tomography (CT) based multi modal medical image fusion using synchronized anisotropic diffusion equation (SADE) with dual tree dual-tree complex wavelet transform (DT-CWT) decomposition. Initially, source images are divided into multiple bands by using DT-CWT approach. In addition, SADE process is applied to extract the approximate and detailed layers. Further, principal component analysis (PCA) is applied to extract the eigen vectors. Finally, PCA fusion rule is applied to get the fused outcome. The simulation results show that proposed fusion results shows better subjective and object performance as compared to conventional fusion methods.
A CNN and Image-Based Approach for Malware Analysis

Malware attacks have various types, patterns, and volumes and have become more sophisticated and severe. Using machine learning to classify and detect malware is one of the approaches to mitigate malware attacks. However, malware classification suffers from some challenges such as the time required in manipulating a huge number of malware files. This paper proposes a Convolutional Neural Network (CNN) model and a pre-processing approach to solve the aforementioned issue. The contribution of this paper is based on converting the dataset into RGB images followed by scale maximization step. In addition, the paper proposes a preprocessing approach for the input datum of images. The results prove that the proposed preprocessing methods have a strong impact on enhancing the overall accuracy by increasing the accuracy from 92.5% to 98%.
Scalable Multi-Agent Reinforcement Learning for Dynamic Coordinated Multipoint Clustering

Reinforcement learning (RL) is a widely investigated intelligent algorithm and proved to be useful in the wireless communication area. However, for optimization problems in large-scale multi-cell networks whose dimension increases exponentially, it is unrealistic to employ a conventional centralized RL algorithm and make decisions for the entire network. Multi-agent RL, which allows distribute decision-making, is expected to solve the scalability problem but with performance issues due to the unknown global information, i.e., non-stationary environment. In this paper, we propose a parameter-sharing multi-agent RL for grouping decisions of coordinated multi-point in a large-scale network, where agents jointly serve users to enhance the cell-edge service. By sharing information via parameters, our theoretical and simulation results show that parameter sharing can largely benefit the multi-agent algorithm with convergence proof and convergence speed analysis. To reduce the effect of biased local heterogeneous experience, we also propose a transfer learning method for the parameter sharing process, whose performance of transfer learning algorithms is verified by the simulation results.
The stable M/G/1 queue in heavy traffic and its covariance function

Let X(t) be the virtual waiting-time process of a stable M/G/1 queue. Let R(t) be the covariance function of the stationary process X(t), B(t) the busy-period distribution of X(t); and let E(t) = P{X(t) = 0|X(0) = 0}. For X(t) some heavy-traffic results are given, among which are limiting expressions for R(t) and its derivatives and for B(t) and E(t). These results are used to find the covariance function of stationary Brownian motion on [0, ∞).
Modern Deep Learning Approaches for Symbol Detection in Complex Engineering Drawings

Due to the rapid advances in object detection methods, object recognition in the engineering drawings has gained researchers' attention in recent years. Usually, engineering drawings are analysed using traditional object detection methods. However, complex structures and their connections sometimes make it challenging for the traditional object detection architectures to process the engineering drawings. With the rapid development in deep learning models, more powerful methods- which are able to interpret high-level and complex features- are introduced to address the challenges exhibited by the traditional methods. These models have different training strategies and thus portray varied behaviors. In this paper, we present a comparative analysis of the deep learning models for object recognition in engineering drawings. Furthermore, real-life scenario on how to process and analyses information from specific engineering drawings, namely, piping and instrumentation diagrams (P&IDs) is discussed in details. Finally, future directions are presented which can serve as research guidelines to work in this domain.
Enterprise Architecture Planning based on One Data in Indonesian Higher Education

The higher education institution continues to compete to increase its ranking in the world-class universities. There are many university ranking institutions with different criteria for ranking classification. However, good university governance will greatly help to achieve higher rankings. Good university governance certainly needs to be supported by accurate data and an integrated information system. One data regulation has potential benefits for universities to re-design its process business and information system in order to support data sharing and using integrated data source. This paper uses TOGAF ADM to design enterprise architecture planning based on one data and integrated application. The results show enterprise architecture designed in order to support one data and integrated application that aligned with the university vision and mission to become a world class university.
Multi-domain Fake News Detection for History News Environment Perception

As social networks continue to grow, fake news in different areas on social media is spreading widely. Numerous parts of the actual world, such as politics and daily living, are threatened by fake news. Most traditional fake news detection focuses on a single domain, so identifying multi-domain fake news is receiving increasing attention. To improve the detection of multi-domain fake news, a novel multi-domain fake news detection model is proposed. We created the History News Environment Perception framework by analyzing the relationship between domain news and its news environment sample (HNEP). The approach is that for each post, we build the history and mainstream news environment from all previous posts. We design a perception module for mainstream and history news environments to improve multi-domain fake news prediction by fusing perceptual representations via domain gates. The experimental results demonstrate that HNEP can enhance the performance of multi-domain fake news detection.
A Systematic Literature Review of Barriers and Drivers E-Government in Developing Countries: TOE Framework Perspective

The adoption and implementation of e-government is a necessity for governments in developing countries. However, there are many barrier factors faced in its implementation. At the same time, there are also driver factors that can support it. We used the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) to identify barriers and drivers in e-government implementation in developing countries. Several theories or frameworks are used in related studies, one of which is the Technology-Organization-Environment (TOE) framework. This study reveals that the previous studies were still focused on the municipal and central government levels. This study also found that there are 15 barriers and 17 driver factors. Based on TOE framework, we classified 7 factors in the technology dimension, 13 factors in the organization dimension, and 10 factors in the environment dimension. These findings can help other developing countries identify the barriers and potentials that can guide them to successful use of e-government.
IoTZeroJar: Towards a Honeypot Architecture for Detection of Zero-Day Attacks in IoT

IoT enables the communication of electronic devices and sensors with the Internet using standard protocols to achieve autonomy, robustness, and reliable data exchange among devices and real applications. The wide variety of IoT devices has led to raising concerns about the security of interconnected devices. IoT manufacturers have been increasing recently, which has resulted in building IoT devices with different standards, protocols, features, and technologies. However, the lack of implementation of security features for IoT devices has led the IoT devices to be susceptible to attacks and targeted by adversaries. In order to provide an efficient honeypot-based solution, it should benefit from the malicious traffic in the filtering phase to detect zero-day attacks. In this paper, we propose IoTZeroJar, a honeypot system to detect the attacker's malicious activities and analyze zero-day attacks.
A Novel Silicon on Glass Electrostatic MEMS for Energy Harvesting in Leadless Pacemakers

In the present work we report the fabrication and characterization of a novel MEMS device intended to power leadless pacemakers. The presented device has the dimensions to fit inside a capsule implantable via cardiac catheterization. To our knowledge this is the first electrostatic MEMS energy harvester allowing such application. We show that the presented device has potential to match the average power consumption of commercial leadless pacemakers.
Offering New Routing Method in Ad hoc Networks using Ant Colony Algorithm

The aim of this study is to provide a novel method routing in ad hoc networks using ant colony algorithm. Hence for this study the researcher attempts to discover and create routes with less number of crossings, nodes sustainable and less energy transfer, to reduce latency end-to-end, save bandwidth and to extend the life and increase the lifetime of the network nodes. Research methodology for simulation algorithm has been OPNET software. Therefore, the proposed algorithm`s performance was compared with one of the most routing algorithms in mobile ad hoc networks Ant Hoc Net. The results showed that the proposed algorithm compared with Ant Hoc Net has more end-to-end delay, more package shipping, and less routing overhead can reduce energy consumption and thus increases the lifetime of the network nodes. The results of this study indicate that the latency end-to-end, saving bandwidth and increasing lifetime of nodes and network lifetime can be predicted by the proposed algorithm.